Apparatus and methods for real-time image generation

Abstract

Aspects of present disclosure relate to real-time image generation. An exemplary apparatus for real time image generation includes at least an optical system, a slide port configured to hold a slide, an actuator mechanism mechanically connected to a mobile element, a user interface comprising an input interface and an output interface, at least processor configured to: using the at least an optical system, capture a first image of the slide at a first position, modify the first image, using the output interface, display the first image to a user, using the input interface, receive a parameter set from the user.

Description

[0001]APPARATUS AND METHODS FOR REAL-TIME IMAGE GENERATION CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority of U.S. Application Serial No.18 / 588,030, filed on February 27, 2024, and entitled “APPARATUS AND METHODS FOR REAL-TIME IMAGE GENERATION” and U.S. Application Serial No.18 / 598,307, filed on March 7, 2024, and entitled “APPARATUS AND METHODS FOR REAL-TIME IMAGE GENERATION,” each of which is a continuation of U.S. Nonprovisional Application Serial No.18 / 226,100, filed on July 25, 2023, now U.S. Patent 11,947,099, issued on April 2, 2024, and entitled “APPARATUS AND METHODS FOR REAL-TIME IMAGE GENERATION,” each of which is incorporated by reference herein in its entirety. FIELD OF THE INVENTION The present invention generally relates to the field of real-time image generation. In particular, the present invention is directed to apparatus and methods for real-time image generation. BACKGROUND Intraoperative diagnostic assessment of tissue is of immense value and several methods have been developed to address the 12-48 hour time-lapse for obtaining high-quality formalin- fixed paraffin-embedded (FFPE) tissue. Recent AI-enabled methods reduce this wait time to minutes making it potentially possible for intraoperative use. However, these approaches still have deficiencies that need to be addressed. For one, they still require a scanning step that digitizes the image followed by a generation step which is then followed by a human expert visually examining the digitized slide. Additionally, they need to rescan the frozen section if the separate digitization process is performed on an image at a suboptimal focal length. SUMMARY OF THE DISCLOSURE In an aspect, an apparatus for real time image generation is disclosed. The apparatus includes at least an optical system, a slide port configured to hold a slide, an actuator mechanism mechanically connected to a mobile element, a user interface including an input interface and an output interface, at least a processor and a memory communicatively connected to the at least processor, the memory containing instructions configuring the at least processor to using the at 1 Attorney Docket No.1519-032PCT1 least an optical system, capture a first image of the slide at a first position, modify the first image, using the output interface, display the first image to a user, using the input interface, receive a parameter set from the user, using the actuator mechanism, move the mobile element into a second position, wherein the second position is determined based on the parameter set, using the at least an optical system, capture a second image of the slide at the second position, modify the second image and using the output interface, display the second image to the user. In some embodiments, moving the mobile element into the second position may include positioning, using the actuator mechanism, the slide relative to the at least an optical system such that a region of interest defined by the parameter set is within a frame of at least an optical sensor of the at least an optical system and setting a zoom of the at least an optical system based on a level of zoom specified by the parameter set. In some embodiments, moving the mobile element into a second position may include setting a focus height of the at least an optical system based on a focus height specified by the parameter set. In some embodiments, moving the mobile element into a second position may include detecting a sample height at the region of interest; and setting a focus height of the at least an optical system based on the sample height at the region of interest. In some embodiments, the second image may be captured using a higher level of zoom than that used to capture the first image. In some embodiments, modifying the second image may include replacing at least a region of the first image with the second image to create a hybrid second image and displaying the hybrid second image to the user. In some embodiments, modifying the first image may include receiving, using the user interface, an annotation instruction from the user and adding an annotation to the first image based on the annotation instruction. In some embodiments, the memory may contain instructions configuring the at least a processor to train an annotation machine learning model using a training dataset including image data associated with a feature depicted by the image, input into the annotation machine learning model an image selected from the list consisting of the first image, the second image, and the hybrid image, receive from the annotation machine learning model a datum identifying a feature depicted by the image selected from the list consisting of the first image, the second image, and the hybrid image and add an annotation to the first image, as a function of the image feature. In some embodiments, modifying the first image may include removing a first artifact from the first image, wherein removing the first artifact may include inputting the first image into an artifact removal machine learning model and receiving, from the artifact removal 2 Attorney Docket No.1519-032PCT1 machine learning model, a version of first image lacking the artifact. In some embodiments, modifying the second image may include removing a second artifact from the second image, wherein removing the second artifact may include inputting the second image into an artifact removal machine learning model and receiving from the artifact removal machine learning model a version of second image lacking the artifact. In another aspect, a method of real time image generation is disclosed. The method includes using at least a processor and at least an optical system, capturing a first image of the slide at a first position, using the at least a processor, modify the first image, using the at least a processor and an output interface, displaying the first image to a user, using the at least a processor and an input interface, receiving a parameter set from the user, using the at least a processor and an actuator mechanism, moving the mobile element into a second position, wherein the second position is determined based on the parameter set, using the at least a processor and the at least an optical system, capturing a second image of the slide at the second position, using the at least a processor, modify the first image and using the at least a processor and the output interface, displaying the second image to the user. In some embodiments, moving the mobile element into the second position may include positioning, using the actuator mechanism, the slide relative to the at least an optical system such that a region of interest defined by the parameter set is within a frame of at least an optical sensor of the at least an optical system and setting a zoom of the at least an optical system based on a level of zoom specified by the parameter set. In some embodiments, moving the mobile element into a second position may include setting a focus height of the at least an optical system based on a focus height specified by the parameter set. In some embodiments, moving the mobile element into a second position may include detecting a sample height at the region of interest and setting a focus height of the at least an optical system based on the sample height at the region of interest. In some embodiments, the second image may be captured using a higher level of zoom than that used to capture the first image. In some embodiments, modifying the second image may include replacing at least a region of the first image with the second image to create a hybrid second image and displaying the hybrid second image to the user. In some embodiments, modifying the first image may include receiving, using the user interface, an annotation instruction from the user and adding an annotation to an image selected from to the first image, based on the annotation instruction. In some embodiments, modifying the first image may include training an 3 Attorney Docket No.1519-032PCT1 annotation machine learning model using a training dataset including image data associated with a feature depicted by the image, inputting into the annotation machine learning model an image selected from the list consisting of the first image, the second image, and the hybrid second image, receiving from the annotation machine learning model a datum identifying a feature depicted by the image selected from the list consisting of the first image, the second image, and the hybrid second image and adding an annotation to the first image, as a function of the image feature. In some embodiments, modifying the first image may include removing a first artifact from the first image, wherein removing the first artifact may include inputting the first image into an artifact removal machine learning model and receiving, from the artifact removal machine learning model, a version of first image lacking the artifact. In some embodiments, modifying the second image may include removing a second artifact from the second image, wherein removing the second artifact may include inputting the second image into an artifact removal machine learning model and receiving from the artifact removal machine learning model a version of second image lacking the artifact. In another aspect, another apparatus for real time image generation is disclosed. The apparatus includes at least an optical system, a slide port configured to hold a slide, an actuator mechanism mechanically connected to a mobile element, a user interface including an input interface and an output interface, wherein the input interface is configured to receive a parameter set from a user, at least a processor and a memory communicatively connected to the at least processor, the memory containing instructions configuring the at least processor to using the at least an optical system, capture a first image of the slide at a first position, modify the first image, using the output interface, display the modified first image to the user, using the actuator mechanism, move the mobile element into a second position, wherein the second position is determined based on the parameter set. In some embodiments, moving the mobile element into the second position may include positioning, using the actuator mechanism, the slide relative to the at least an optical system such that a region of interest defined by the parameter set is within a frame of at least an optical sensor of the at least an optical system and setting a zoom of the at least an optical system based on a level of zoom specified by the parameter set. In some embodiments, the input from the user may include an audio input, and wherein the audio input may be interpreted using an automatic speech recognition function. In some embodiments, the parameter set may include an image capture parameter, and wherein the first image may be 4 Attorney Docket No.1519-032PCT1 captured using the image capture parameter. In some embodiments, the memory may contain instructions configuring the at least processor to transmit the first image to an image processing module, wherein the image processing module may be configured to determine a region of interest is by isolating a feature of interest from the first image and segmenting the region of interest depicting a feature of interest into a plurality of sub-regions. In some embodiments, the memory may contain instructions configuring the at least processor to determine a degree of quality of depiction of the region of interest of the first image. In some embodiments, modifying the first image may include receiving, using the user interface, an annotation instruction from the user and adding an annotation to the first image based on the annotation instruction. In some embodiments, the memory may contain instructions configuring the at least a processor to train an annotation machine learning model using a training dataset including image data associated with a feature depicted by the image, input into the annotation machine learning model an image selected from the list consisting of the first image, the second image, and the hybrid image, receive from the annotation machine learning model a datum identifying a feature depicted by the image selected from the list consisting of the first image, the second image, and the hybrid image and add an annotation to the first image, as a function of the image feature. In some embodiments, modifying the first image may include removing a first artifact from the first image, wherein removing the first artifact may include inputting the first image into an artifact removal machine learning model and receiving, from the artifact removal machine learning model, a version of first image lacking the artifact. In some embodiments, modifying the first image may include isolating a feature of interest from an image and determining a feature of interest using an edge detection technique. In another aspect, another method of real time image generation is disclosed. The method includes using at least a processor and at least an optical system, capturing a first image of the slide at a first position, using the at least a processor, modifying the first image, using the at least a processor and an input interface, receiving a parameter set from the user, using the at least a processor and an actuator mechanism, moving the mobile element into a second position, wherein the second position is determined based on the parameter set and using an output interface, displaying the modified first image to a user. In some embodiments, moving the mobile element into the second position may include positioning, using the actuator mechanism, the slide relative to the at least an optical system such that a region of interest defined by the 5 Attorney Docket No.1519-032PCT1 parameter set is within a frame of at least an optical sensor of the at least an optical system and setting a zoom of the at least an optical system based on a level of zoom specified by the parameter set. In some embodiments, the input from the user may include an audio input, and wherein the audio input may be interpreted using an automatic speech recognition function. In some embodiments, the parameter set may include an image capture parameter, and wherein the first image may be captured using the image capture parameter. In some embodiments, the method may further include using the at least a processor, transmitting the first image to an image processing module, wherein the image processing module may be configured to determine a region of interest is by isolating a feature of interest from the first image and segmenting the region of interest depicting a feature of interest into a plurality of sub-regions. In some embodiments, the method may further include using the at least a processor, determining a degree of quality of depiction of the region of interest of the first image. In some embodiments, modifying the first image may include receiving, using the user interface, an annotation instruction from the user and adding an annotation to an image selected from to the first image, based on the annotation instruction. In some embodiments, modifying the first image may include training an annotation machine learning model using a training dataset including image data associated with a feature depicted by the image, inputting into the annotation machine learning model an image selected from the list consisting of the first image, the second image, and the hybrid second image, receiving from the annotation machine learning model a datum identifying a feature depicted by the image selected from the list consisting of the first image, the second image, and the hybrid second image and adding an annotation to the first image, as a function of the image feature. In some embodiments, modifying the first image may include removing a first artifact from the first image, wherein removing the first artifact may include inputting the first image into an artifact removal machine learning model and receiving, from the artifact removal machine learning model, a version of first image lacking the artifact. In some embodiments, modifying the first image may include isolating a feature of interest from an image and determining a feature of interest using an edge detection technique. In another aspect, another apparatus for real time image generation is disclosed. The apparatus includes at least an optical system, a slide port configured to hold a slide, an actuator mechanism mechanically connected to a mobile element, a user interface, at least a processor and a memory communicatively connected to the at least a processor, the memory containing 6 Attorney Docket No.1519-032PCT1 instructions configuring the at least a processor to using the at least an optical system, capture a first image of the slide at a first position, receive a first parameter set, using the actuator mechanism, move the mobile element into a second position, wherein the second position is determined based on the first parameter set, using the at least an optical system, capture a second image of the slide at the second position and using the user interface, display the second image to a user. In some embodiments, the first image may include an image of the entire slide. In some embodiments, the second image may include a plurality of images of a region of interest of the slide at differing focus depths for each image of the plurality of images. In some embodiments, the memory may contain further instructions configuring the at least a processor to add an annotation to the second image based as a function of an annotation instruction. In some embodiments, the memory may contain further instructions configuring the at least a processor to identify at least an artifact within the first image using a machine vision system. In some embodiments, the memory may contain further instructions configuring the at least a processor to remove the at least an artifact from the first image. In some embodiments, the memory may contain further instructions configuring the at least a processor to identify a second parameter set as a function of the first parameter set, wherein the second parameter set may include a level of zoom associated with a region of interest of the slide. In some embodiments, the memory may contain further instructions configuring the at least a processor to modify the second image, wherein modifying the second image may include replacing at least a region of the first image with the second image to create a hybrid image. In some embodiments, moving the mobile element into the second position may include setting a focus height of the at least an optical system based on a focus height specified by the first parameter set. In some embodiments, moving the mobile element into the second position may include positioning, using the actuator mechanism, the at least an optical system relative to the slide such that a region of interest of the slide defined by the first parameter set is within a frame of the at least an optical sensor of the at least an optical system. In another aspect, another method for real time image generation is disclosed. The method includes capturing, by at least a processor, a first image of the slide at a first position using at least an optical system, receiving, by the at least a processor, a first parameter set, moving, by the at least a processor, a mobile element into a second position, wherein the second position is determined based on the first parameter set using an actuator mechanism, capturing, 7 Attorney Docket No.1519-032PCT1 by the at least a processor, a second image of the slide at the second position using at least an optical system and displaying, by the at least a processor, the second image to a user using the user interface. In some embodiments, the first image may include an image of the entire slide. In some embodiments, the second image may include a plurality of images of a region of interest of the slide at differing focus depths for each image of the plurality of images. In some embodiments, the method further may include adding, by the at least a processor, an annotation to the second image based as a function of an annotation instruction. In some embodiments, the method may further include identifying, by the at least a processor, at least an artifact within the first image using a machine vision system. In some embodiments, the method may further include removing, by the at least a processor, the at least an artifact from the first image. In some embodiments, the method may further include identifying, by the at least a processor, a second parameter set as a function of the first parameter set, wherein the second parameter set may include a level of zoom associated with a region of interest within the slide. In some embodiments, the method may further include modifying, by the at least a processor, the second image, wherein modifying the second image may include replacing at least a region of the first image with the second image to create a hybrid image. In some embodiments, moving the mobile element into the second position may include setting a focus height of the at least an optical system based on a focus height specified by the first parameter set. In some embodiments, moving the mobile element into the second position may include positioning, using the actuator mechanism, the at least an optical system relative to the slide such that a region of interest of the slide defined by the first parameter set is within a frame of the at least an optical sensor of the at least an optical system. In another aspect, another apparatus for real time image generation is disclosed. The apparatus includes at least an optical system, a slide port configured to hold a slide, an actuator mechanism mechanically connected to a mobile element, a user interface, at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to using the at least an optical system, capture a first image of the slide at a first position, receive a first parameter set, using the actuator mechanism, move the mobile element into a second position, wherein the second position is determined based on the parameter set, using the at least an optical system, capture a second image of the slide at the second position, using the at least an optical system, generate a hybrid 8 Attorney Docket No.1519-032PCT1 image by replacing at least a region of the first image with the second image, wherein generating the hybrid image includes identifying at least an artifact within the hybrid image and removing the at least an artifact from the hybrid image, wherein removing the at least an artifact includes receiving the first image by an artifact removal machine learning model, wherein the artifact removal machine learning model includes a generative machine learning model and removing the at least an artifact from the hybrid image using the artifact removal machine learning model and using the user interface, display the hybrid image to a user. In some embodiments, removing the at least an artifact may include generating synthetic training data using the artifact removal machine learning model, wherein the synthetic training data may include a set of images containing artifacts correlated to a set of images without artifacts and training the artifact removal machine learning model using the synthetic training data. In some embodiments, the first image may include an image of the entire slide. In some embodiments, the second image may include a plurality of images of the region of interest of the slide at differing focus depths for each image of the plurality of images. In some embodiments, the memory may contain further instructions configuring the at least a processor to add an annotation to the second image based as a function of an annotation instruction. In some embodiments, the memory may contain further instructions configuring the at least a processor to identify the at least an artifact within the first image using a machine vision system. In some embodiments, the memory may contain further instructions configuring the at least a processor to identify a second parameter set as a function of the first parameter set, wherein the second parameter set may include a level of zoom associated with the region of interest within the slide. In some embodiments, the memory may contain further instructions configuring the at least a processor to identify a second parameter set as a function of the first parameter set, wherein the second parameter set may include a focus depth associated with the region of interest within the slide. In some embodiments, moving the mobile element into the second position may include setting a focus height of the at least an optical system based on a focus height specified by the first parameter set. In some embodiments, moving the mobile element into the second position may include positioning, using the actuator mechanism, the at least an optical system relative to the slide such that the region of interest of the slide defined by the first parameter set is within a frame of the at least an optical sensor of the at least an optical system. 9 Attorney Docket No.1519-032PCT1 In another aspect, another method for real time image generation is disclosed. The method includes capturing, using at least a processor and at least an optical system, a first image of the slide at a first position, receiving, by the at least a processor, a first parameter set, removing, by the at least a processor, a at least an artifact from the first image, wherein removing the at least an artifact includes receiving the first image by an artifact removal machine learning model, wherein the artifact removal machine learning model includes a generative machine learning model and removing the at least an artifact from the first image using the artifact removal machine learning model, moving, by the at least a processor, a mobile element into a second position, wherein the second position is determined based on the first parameter set using an actuator mechanism, capturing, by the at least a processor, a second image of the slide at the second position using at least an optical system, generating, by the at least a processor, a hybrid image by replacing at least a region of interest of the first image with the second image to create a hybrid image using the at least an optical system, wherein generating the hybrid image includes identifying at least an artifact within the hybrid image and removing the at least an artifact from the hybrid image, wherein removing the at least an artifact includes receiving the first image by an artifact removal machine learning model, wherein the artifact removal machine learning model includes a generative machine learning model and removing the at least an artifact from the hybrid image using the artifact removal machine learning model and displaying, by the at least a processor, the hybrid image to a user using the user interface. In some embodiments, removing the at least an artifact may further include generating synthetic training data using the artifact removal machine learning model, wherein the synthetic training data may be comprised of images including artifacts correlated to images without artifacts and training the artifact removal machine learning model using the synthetic training data. In some embodiments, the first image may include an image of the entire slide. In some embodiments, the second image may include a plurality of images of a region of interest of the slide at differing focus depths for each image of the plurality of images. In some embodiments, the method may further include adding, by the at least a processor, an annotation to the second image based as a function of an annotation instruction. In some embodiments, the method may further include identifying, by the at least a processor, the at least an artifact within the first image using a machine vision system. In some embodiments, the method may further include identifying, by the at least a processor, a second parameter set as a function of the first parameter set, wherein the second parameter set 10 Attorney Docket No.1519-032PCT1 may include a level of zoom associated with the region of interest within the slide. In some embodiments, the method may further include identifying, by the at least a processor, a second parameter set as a function of the first parameter set, wherein the second parameter set may include a focus depth associated with the region of interest within the slide. In some embodiments, moving the mobile element into the second position may include setting a focus height of the at least an optical system based on a focus height specified by the first parameter set. In some embodiments, moving the mobile element into the second position may include positioning, using the actuator mechanism, the at least an optical system relative to the slide such that a region of interest of the slide defined by the first parameter set is within a frame of the at least an optical sensor of the at least an optical system. The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the description below. Other features and advantages of the subject matter described herein will be apparent from the description and drawings, and from the claims. DESCRIPTION OF DRAWINGS For the purpose of illustrating the invention, the drawings show aspects of one or more embodiments of the invention. However, it should be understood that the present invention is not limited to the precise arrangements and instrumentalities shown in the drawings, wherein: FIG.1 illustrates a diagram depicting an exemplary apparatus for real-time image generation; FIG.2 illustrates a diagram depicting an exemplary machine learning model; FIG.3 illustrates a diagram depicting an exemplary neural network; FIG.4 illustrates a diagram depicting an exemplary neural network node; FIG.5 illustrates a diagram depicting an exemplary method of real-time image generation; FIG.6 illustrates another exemplary method of real-time image generation; FIG.7 illustrates yet another exemplary method of real-time image generation; FIG.8 illustrates a block diagram of an exemplary embodiment of an apparatus for generating a confidence score associated with a scanned label; FIG.9A-C illustrate various diagrammatic representations of an exemplary embodiment of an imaging device for image generation of a specimen in accordance with one or more embodiments of the present disclosure; 11 Attorney Docket No.1519-032PCT1 FIG.10 illustrates a block diagram of an exemplary embodiment of an apparatus for detecting associations among datasets of different types; and FIG.11 is a block diagram of a computing system that can be used to implement any one or more of the methodologies disclosed herein and any one or more portions thereof. The drawings are not necessarily to scale and may be illustrated by phantom lines, diagrammatic representations, and fragmentary views. In certain instances, details that are not necessary for an understanding of the embodiments or that render other details difficult to perceive may have been omitted. Like reference symbols in the various drawings indicate like elements. DETAILED DESCRIPTION At a high level, aspects of the present disclosure are directed to apparatus and methods for real time image generation. Apparatus described herein may generate images of slides 116 and / or samples on slides. In an embodiment, said apparatus may generate a plurality of images, make adjustments to the plurality of images, and display plurality of images to a user in real time as the user manipulates an input interface 128. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples. Referring now to FIG.1, an exemplary embodiment of an apparatus 100 for real time image generation is illustrated. Apparatus 100 may include a computing device. Apparatus 100 may include a processor 104. Processor 104 may include, without limitation, any processor 104 described in this disclosure. Processor 104 may be included in a computing device. Apparatus 100 may include at least a processor 104 and a memory 108 communicatively connected to the at least a processor 104, the memory 108 containing instructions 112 configuring the at least a processor 104 to perform one or more processes described herein. Computing device may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and / or system on a chip (SoC) as described in this disclosure. Computing device may include, be included in, and / or communicate with a mobile device such as a mobile telephone or smartphone. Computing device may include a single computing device operating independently, or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices. Computing device may interface or communicate with one or more additional devices as 12 Attorney Docket No.1519-032PCT1 described below in further detail via a network interface device. Network interface device may be utilized for connecting computing device to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone / voice provider (e.g., a mobile communications provider data and / or voice network), a direct connection between two computing devices, and any combinations thereof. A network may employ a wired and / or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software etc.) may be communicated to and / or from a computer and / or a computing device. Computing device may include but is not limited to, for example, a computing device or cluster of computing devices in a first location and a second computing device or cluster of computing devices in a second location. Computing device may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. Computing device may distribute one or more computing tasks as described below across a plurality of computing devices of computing device, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices. Computing device may be implemented, as a non-limiting example, using a “shared nothing” architecture. With continued reference to FIG.1, computing device may be designed and / or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, computing device may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and / or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and / or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and / or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Computing device may perform any step or sequence of steps as described in this disclosure in parallel, such 13 Attorney Docket No.1519-032PCT1 as simultaneously and / or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and / or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and / or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and / or parallel processing. Still referring to FIG.1, as used in this disclosure, “communicatively connected” means connected by way of a connection, attachment or linkage between two or more relata which allows for reception and / or transmittance of information therebetween. For example, and without limitation, this connection may be wired or wireless, direct or indirect, and between two or more components, circuits, devices, systems, and the like, which allows for reception and / or transmittance of data and / or signal(s) therebetween. Data and / or signals therebetween may include, without limitation, electrical, electromagnetic, magnetic, video, audio, radio and microwave data and / or signals, combinations thereof, and the like, among others. A communicative connection may be achieved, for example and without limitation, through wired or wireless electronic, digital or analog, communication, either directly or by way of one or more intervening devices or components. Further, communicative connection may include electrically coupling or connecting at least an output of one device, component, or circuit to at least an input of another device, component, or circuit. For example, and without limitation, via a bus or other facility for intercommunication between elements of a computing device. Communicative connecting may also include indirect connections via, for example and without limitation, wireless connection, radio communication, low power wide area network, optical communication, magnetic, capacitive, or optical coupling, and the like. In some instances, the terminology “communicatively coupled” may be used in place of communicatively connected in this disclosure. Still referring to FIG.1, in some embodiments, apparatus 100 may be used to generate an image of slide 116 and / or a sample on slide 116. As used herein, a “slide” is a container or surface holding a sample of interest. In some embodiments, slide 116 may include a glass slide. In some embodiments, slide 116 may include a formalin fixed paraffin embedded slide. In some embodiments, a sample on slide 116 may be stained. In some embodiments, slide 116 may be 14 Attorney Docket No.1519-032PCT1 substantially transparent. In some embodiments, slide 116 may include a thin, flat, and substantially transparent glass slide. In some embodiments, a transparent cover may be applied to slide 116 such that a sample is between slide 116 and this cover. In some embodiments, a sample on slide 116 may include a tissue sample. In some embodiments, sample on slide 116 may be frozen. Still referring to FIG.1, in some embodiments, slide 116 and / or a sample on slide 116 may be illuminated. In some embodiments, apparatus 100 may include a light source. As used herein, a “light source” is any device configured to emit electromagnetic radiation. In some embodiments, light source may emit a light having substantially one wavelength. In some embodiments, light source may emit a light having a wavelength range. Light source may emit, without limitation, ultraviolet light, visible light, and / or infrared light. In non-limiting examples, light source may include a light-emitting diode (LED), an organic LED (OLED) and / or any other light emitter. Such a light source may be configured to illuminate slide 116 and / or sample on slide 116. In a non-limiting example, light source may illuminate slide 116 and / or sample on slide 116 from below. Still referring to FIG.1, in some embodiments, apparatus 100 may include at least an optical system 120. As used in this disclosure, an "optical system" is an arrangement of one or more components which together act upon or employ electromagnetic radiation, such as light (e.g., visible light, infrared light, UV light, or the like). An optical system may include one or more optical elements, including without limitation lenses, mirrors, windows, filters, and the like. An optical system may form an optical image that corresponds to an optical object. For instance, an optical system may form an optical image at or upon an optical sensor, which can capture, e.g., digitize, the optical image. In some cases, optical system may have at least a magnification. For instance, optical system may include an objective (e.g., microscope objective) and one or more reimaging optical elements that together produce an optical magnification. In some cases, optical magnification may be referred to herein as zoom. As used herein, an “optical sensor” is a device that measures light and converts the measured light into one or more signals; one or more signals may include, without limitation, one or more electrical signals. In some embodiments, optical sensor 120 may include at least a photodetector. As used herein, a “photodetector” is a device that is sensitive to light and thereby able to detect light. In some embodiments, a photodetector may include a photodiode, a photoresistor, a photosensor, a 15 Attorney Docket No.1519-032PCT1 photovoltaic chip, and the like. In some embodiments, optical sensor 120 may include a plurality of photodetectors. Optical sensor 120 may include, without limitation, a camera. Optical sensor 120 may be in electronic communication with at least a processor 104 of apparatus 100. As used herein, “electronic communication” as used in this disclosure is a shared data connection between two or more devices. In some embodiments, apparatus 100 may include two or more optical sensors 120. Still referring to FIG.1, in some embodiments, optical system 120 may include a camera. In some cases, a camera may include one or more optics. Exemplary non-limiting optics include spherical lenses, aspherical lenses, reflectors, polarizers, filters, windows, aperture stops, and the like. In some embodiments, one or more optics associated with a camera may be adjusted in order to, in non-limiting examples, change the zoom, depth of field, and / or focus distance of the camera. In some embodiments, one or more of such settings may be configured to detect a feature of a sample on slide 116. In some embodiments, one or more of such settings may be configured based on a parameter set, as described below. In some embodiments, camera may capture images at a low depth of field. In a non-limiting example, camera may capture images such that a first depth of sample is in focus and a second depth of sample is out of focus. In some embodiments, an autofocus mechanism may be used to determine focus distance. In some embodiments, focus distance may be set by parameter set. In some embodiments, camera may be configured to capture a plurality of images at different focus distances. In a non-limiting example, camera may capture a plurality of images at different focus distances, such that images are captured where each focus depth of the sample is in focus in at least one image. In some embodiments, at least a camera may include an image sensor. Exemplary non-limiting image sensors include digital image sensors, such as without limitation charge-coupled device (CCD) sensors and complimentary metal-oxide-semiconductor (CMOS) sensors. In some embodiments, a camera may be sensitive within a non-visible range of electromagnetic radiation, such as without limitation infrared. Still referring to FIG.1, as used herein, “image data” is information representing at least a physical scene, space, and / or object. Image data may include, for example, information representing a sample, slide 116, or region of a sample or slide. In some cases, image data may be generated by a camera. “Image data” may be used interchangeably through this disclosure with “image,” where image is used as a noun. An image may be optical, such as without 16 Attorney Docket No.1519-032PCT1 limitation where at least an optic is used to generate an image of an object. An image may be digital, such as without limitation when represented as a bitmap. Alternatively, an image may be comprised of any media capable of representing a physical scene, space, and / or object. Alternatively, where “image” is used as a verb, in this disclosure, it refers to generation and / or formation of an image. Still referring to FIG.1, in some embodiments, apparatus 100 may include a slide port 144. In some embodiments, slide port 144 may be configured to hold slide 116. In some embodiments, slide port 144 may include one or more alignment features. As used herein, an “alignment feature” is a physical feature that helps to secure a slide in place and / or align a slide with another component of an apparatus. In some embodiments, alignment feature may include a component which keeps slide 116 secure, such as a clamp, latch, clip, recessed area, or another fastener. In some embodiments, slide port 144 may allow for easy removal or insertion of slide 116. In some embodiments, slide port 144 may include a transparent surface through which light may travel. In some embodiments, slide 116 may rest on and / or may be illuminated by light traveling through such a transparent surface. In some embodiments, slide port 144 may be mechanically connected to an actuator mechanism 124 as described below. Still referring to FIG.1, in some embodiments, apparatus 100 may include an actuator mechanism 124. As used herein, an “actuator mechanism” is a mechanical component configured to change the relative position of a slide and an optical system. In some embodiments, actuator mechanism 124 may be mechanically connected to slide 116, such as slide 116 in slide port 144. In some embodiments, actuator mechanism 124 may be mechanically connected to slide port 144. For example, actuator mechanism 124 may move slide port 144 in order to move slide 116. In some embodiments, actuator mechanism 124 may be mechanically connected to at least an optical system 120. In some embodiments, actuator mechanism 124 may be mechanically connected to a mobile element. As used herein, a “mobile element” refers to any movable or portable object, component, and device within apparatus 100 such as, without limitation, a slide, a slide port, or an optical system. In some embodiments, a mobile element may move such that optical system 120 is positioned correctly with respect to slide 116 such that optical system 120 may capture an image of slide 116 according to a parameter set. In some embodiments, actuator mechanism 124 may be mechanically connected to an item selected from the list consisting of slide port 144, slide 116, and at least an optical system 120. In some 17 Attorney Docket No.1519-032PCT1 embodiments, actuator mechanism 124 may be configured to change the relative position of slide 116 and optical system 120 by moving slide port 144, slide 116, and / or optical system 120. Still referring to FIG.1, actuator mechanism 124 may include a component of a machine that is responsible for moving and / or controlling a mechanism or system. Actuator mechanism 124 may, in some embodiments, require a control signal and / or a source of energy or power. In some cases, a control signal may be relatively low energy. Exemplary control signal forms include electric potential or current, pneumatic pressure or flow, or hydraulic fluid pressure or flow, mechanical force / torque or velocity, or even human power. In some cases, an actuator may have an energy or power source other than control signal. This may include a main energy source, which may include for example electric power, hydraulic power, pneumatic power, mechanical power, and the like. In some embodiments, upon receiving a control signal, actuator mechanism 124 responds by converting source power into mechanical motion. In some cases, actuator mechanism 124 may be understood as a form of automation or automatic control. Still referring to FIG.1, in some embodiments, actuator mechanism 124 may include a hydraulic actuator. A hydraulic actuator may consist of a cylinder or fluid motor that uses hydraulic power to facilitate mechanical operation. Output of hydraulic actuator mechanism 124 may include mechanical motion, such as without limitation linear, rotatory, or oscillatory motion. In some embodiments, hydraulic actuator may employ a liquid hydraulic fluid. As liquids, in some cases, are incompressible, a hydraulic actuator can exert large forces. Additionally, as force is equal to pressure multiplied by area, hydraulic actuators may act as force transformers with changes in area (e.g., cross sectional area of cylinder and / or piston). An exemplary hydraulic cylinder may consist of a hollow cylindrical tube within which a piston can slide. In some cases, a hydraulic cylinder may be considered single acting. Single acting may be used when fluid pressure is applied substantially to just one side of a piston. Consequently, a single acting piston can move in only one direction. In some cases, a spring may be used to give a single acting piston a return stroke. In some cases, a hydraulic cylinder may be double acting. Double acting may be used when pressure is applied substantially on each side of a piston; any difference in resultant force between the two sides of the piston causes the piston to move. Still referring to FIG.1, in some embodiments, actuator mechanism 124 may include a pneumatic actuator mechanism 124. In some cases, a pneumatic actuator may enable considerable forces to be produced from relatively small changes in gas pressure. In some cases, 18 Attorney Docket No.1519-032PCT1 a pneumatic actuator may respond more quickly than other types of actuators, for example hydraulic actuators. A pneumatic actuator may use compressible fluid (e.g., air). In some cases, a pneumatic actuator may operate on compressed air. Operation of hydraulic and / or pneumatic actuators may include control of one or more valves, circuits, fluid pumps, and / or fluid manifolds. Still referring to FIG.1, in some cases, actuator mechanism 124 may include an electric actuator. Electric actuator mechanism 124 may include any of electromechanical actuators, linear motors, and the like. In some cases, actuator mechanism 124 may include an electromechanical actuator. An electromechanical actuator may convert a rotational force of an electric rotary motor into a linear movement to generate a linear movement through a mechanism. Exemplary mechanisms, include rotational to translational motion transformers, such as without limitation a belt, a screw, a crank, a cam, a linkage, a scotch yoke, and the like. In some cases, control of an electromechanical actuator may include control of electric motor, for instance a control signal may control one or more electric motor parameters to control electromechanical actuator. Exemplary non-limitation electric motor parameters include rotational position, input torque, velocity, current, and potential. Electric actuator mechanism 124 may include a linear motor. Linear motors may differ from electromechanical actuators, as power from linear motors is output directly as translational motion, rather than output as rotational motion and converted to translational motion. In some cases, a linear motor may cause lower friction losses than other devices. Linear motors may be further specified into at least 3 different categories, including flat linear motor, U-channel linear motors and tubular linear motors. Linear motors may be directly controlled by a control signal for controlling one or more linear motor parameters. Exemplary linear motor parameters include without limitation position, force, velocity, potential, and current. Still referring to FIG.1, in some embodiments, an actuator mechanism 124 may include a mechanical actuator mechanism 124. In some cases, a mechanical actuator mechanism 124 may function to execute movement by converting one kind of motion, such as rotary motion, into another kind, such as linear motion. An exemplary mechanical actuator includes a rack and pinion. In some cases, a mechanical power source, such as a power take off may serve as power source for a mechanical actuator. Mechanical actuators may employ any number of mechanisms, including for example without limitation gears, rails, pulleys, cables, linkages, and the like. 19 Attorney Docket No.1519-032PCT1 Still referring to FIG.1, in some embodiments, actuator mechanism 124 may be in electronic communication with actuator controls. As used herein, “actuator controls” is a system configured to operate actuator mechanism such that a slide and an optical system reach a desired relative position. In some embodiments, actuator controls may operate actuator mechanism 124 based on input received from a user interface 136. In some embodiments, actuator controls may be configured to operate actuator mechanism 124 such that optical system 120 is in a position to capture an image of an entire sample. In some embodiments, actuator controls may be configured to operate actuator mechanism 124 such that optical system 120 is in a position to capture an image of a region of interest. As used in this disclosure, a “region of interest” is a specific area within a digital image, or a specific area within a slide. In some embodiments, a region of interest may include a region selected by a user or navigated to by a user. Electronic communication between actuator mechanism 124 and actuator controls may include transmission of signals. For example, actuator controls may generate physical movements of actuator mechanism in response to an input signal. In some embodiments, input signal may be received by actuator controls from processor 104 or input interface 128. Still referring to FIG.1, as used in this disclosure, a “signal” is any intelligible representation of data, for example from one device to another. A signal may include an optical signal, a hydraulic signal, a pneumatic signal, a mechanical signal, an electric signal, a digital signal, an analog signal, and the like. In some cases, a signal may be used to communicate with a computing device, for example by way of one or more ports. In some cases, a signal may be transmitted and / or received by a computing device, for example by way of an input / output port. An analog signal may be digitized, for example by way of an analog to digital converter. In some cases, an analog signal may be processed, for example by way of any analog signal processing steps described in this disclosure, prior to digitization. In some cases, a digital signal may be used to communicate between two or more devices, including without limitation computing devices. In some cases, a digital signal may be communicated by way of one or more communication protocols, including without limitation internet protocol (IP), controller area network (CAN) protocols, serial communication protocols (e.g., universal asynchronous receiver-transmitter [UART]), parallel communication protocols (e.g., IEEE 128 [printer port]), and the like. 20 Attorney Docket No.1519-032PCT1 Still referring to FIG.1, in some embodiments, apparatus 100 may perform one or more signal processing steps on a signal. For instance, apparatus 100 may analyze, modify, and / or synthesize a signal representative of data in order to improve the signal, for instance by improving transmission, storage efficiency, or signal to noise ratio. Exemplary methods of signal processing may include analog, continuous time, discrete, digital, nonlinear, and statistical. Analog signal processing may be performed on non-digitized or analog signals. Exemplary analog processes may include passive filters, active filters, additive mixers, integrators, delay lines, compandors, multipliers, voltage-controlled filters, voltage-controlled oscillators, and phase-locked loops. Continuous-time signal processing may be used, in some cases, to process signals which vary continuously within a domain, for instance time. Exemplary non-limiting continuous time processes may include time domain processing, frequency domain processing (Fourier transform), and complex frequency domain processing. Discrete time signal processing may be used when a signal is sampled non-continuously or at discrete time intervals (i.e., quantized in time). Analog discrete-time signal processing may process a signal using the following exemplary circuits sample and hold circuits, analog time-division multiplexers, analog delay lines and analog feedback shift registers. Digital signal processing may be used to process digitized discrete-time sampled signals. Commonly, digital signal processing may be performed by a computing device or other specialized digital circuits, such as without limitation an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a specialized digital signal processor (DSP). Digital signal processing may be used to perform any combination of typical arithmetical operations, including fixed-point and floating-point, real- valued and complex-valued, multiplication and addition. Digital signal processing may additionally operate circular buffers and lookup tables. Further non-limiting examples of algorithms that may be performed according to digital signal processing techniques include fast Fourier transform (FFT), finite impulse response (FIR) filter, infinite impulse response (IIR) filter, and adaptive filters such as the Wiener and Kalman filters. Statistical signal processing may be used to process a signal as a random function (i.e., a stochastic process), utilizing statistical properties. For instance, in some embodiments, a signal may be modeled with a probability distribution indicating noise, which then may be used to reduce noise in a processed signal. 21 Attorney Docket No.1519-032PCT1 Still referring to FIG.1, in some embodiments, apparatus 100 may include a user interface 136. User interface 136 may include output interface 132 and input interface 128. Still referring to FIG.1, in some embodiments, output interface 132 may include one or more elements through which apparatus 100 may communicate information to a user. In a non- limiting example, output interface 132 may include a display. A display may include a high resolution display. A display may output images, videos, and the like to a user. In another non- limiting example, output interface 132 may include a speaker. A speaker may output audio to a user. In another non-limiting example, output interface 132 may include a haptic device. A speaker may output haptic feedback to a user. Still referring to FIG.1, in some embodiments, input interface 128 may include controls for operating apparatus 100. Such controls may be operated by a user. Input interface 128 may include, in non-limiting examples, a camera, microphone, keyboard, touch screen, mouse, joystick, foot pedal, button, dial, and the like. Input interface 128 may accept, in non-limiting examples, mechanical input, audio input, visual input, text input, and the like. In some embodiments, audio inputs into input interface 128 may be interpreted using an automatic speech recognition function, allowing a user to control apparatus 100 via speech. In some embodiments, input interface 128 may approximate controls of a microscope. Still referring to FIG.1, in some embodiments, audio inputs may be processed using automatic speech recognition. In some embodiments, automatic speech recognition may require training (i.e., enrollment). In some cases, training an automatic speech recognition model may require an individual speaker to read text or isolated vocabulary. In some cases, audio training data may include an audio component having an audible verbal content, the contents of which are known a priori by a computing device. Computing device may then train an automatic speech recognition model according to training data which includes audible verbal content correlated to known content. In this way, computing device may analyze a person's specific voice and train an automatic speech recognition model to the person's speech, resulting in increased accuracy. Alternatively, or additionally, in some cases, computing device may include an automatic speech recognition model that is speaker independent. As used in this disclosure, a “speaker independent” automatic speech recognition process does not require training for each individual speaker. Conversely, as used in this disclosure, automatic speech recognition processes that employ individual speaker specific training are “speaker dependent.” 22 Attorney Docket No.1519-032PCT1 Still referring to FIG.1, in some embodiments, an automatic speech recognition process may perform voice recognition or speaker identification. As used in this disclosure, “voice recognition” refers to identifying a speaker, from audio content, rather than what the speaker is saying. In some cases, computing device may first recognize a speaker of verbal audio content and then automatically recognize speech of the speaker, for example by way of a speaker dependent automatic speech recognition model or process. In some embodiments, an automatic speech recognition process can be used to authenticate or verify an identity of a speaker. In some cases, a speaker may or may not include subject. For example, subject may speak within audio inputs, but others may speak as well. Still referring to FIG.1, in some embodiments, an automatic speech recognition process may include one or all of acoustic modeling, language modeling, and statistically based speech recognition algorithms. In some cases, an automatic speech recognition process may employ hidden Markov models (HMMs). As discussed in greater detail below, language modeling such as that employed in natural language processing applications like document classification or statistical machine translation, may also be employed by an automatic speech recognition process. Still referring to FIG.1, an exemplary algorithm employed in automatic speech recognition may include or even be based upon hidden Markov models. Hidden Markov models (HMMs) may include statistical models that output a sequence of symbols or quantities. HMMs can be used in speech recognition because a speech signal can be viewed as a piecewise stationary signal or a short-time stationary signal. For example, over a short time scale (e.g., 10 milliseconds), speech can be approximated as a stationary process. Speech (i.e., audible verbal content) can be understood as a Markov model for many stochastic purposes. Still referring to FIG.1, in some embodiments HMMs can be trained automatically and may be relatively simple and computationally feasible to use. In an exemplary automatic speech recognition process, a hidden Markov model may output a sequence of n-dimensional real- valued vectors (with n being a small integer, such as 10), at a rate of about one vector every 10 milliseconds. Vectors may consist of cepstral coefficients. A cepstral coefficient requires using a spectral domain. Cepstral coefficients may be obtained by taking a Fourier transform of a short time window of speech yielding a spectrum, decorrelating the spectrum using a cosine transform, and taking first (i.e., most significant) coefficients. In some cases, an HMM may have in each 23 Attorney Docket No.1519-032PCT1 state a statistical distribution that is a mixture of diagonal covariance Gaussians, yielding a likelihood for each observed vector. In some cases, each word, or phoneme, may have a different output distribution; an HMM for a sequence of words or phonemes may be made by concatenating an HMMs for separate words and phonemes. Still referring to FIG.1, in some embodiments, an automatic speech recognition process may use various combinations of a number of techniques in order to improve results. In some cases, a large-vocabulary automatic speech recognition process may include context dependency for phonemes. For example, in some cases, phonemes with different left and right context may have different realizations as HMM states. In some cases, an automatic speech recognition process may use cepstral normalization to normalize for different speakers and recording conditions. In some cases, an automatic speech recognition process may use vocal tract length normalization (VTLN) for male-female normalization and maximum likelihood linear regression (MLLR) for more general speaker adaptation. In some cases, an automatic speech recognition process may determine so-called delta and delta-delta coefficients to capture speech dynamics and might use heteroscedastic linear discriminant analysis (HLDA). In some cases, an automatic speech recognition process may use splicing and a linear discriminate analysis (LDA)-based projection, which may include heteroscedastic linear discriminant analysis or a global semi-tied covariance transform (also known as maximum likelihood linear transform [MLLT]). In some cases, an automatic speech recognition process may use discriminative training techniques, which may dispense with a purely statistical approach to HMM parameter estimation and instead optimize some classification-related measure of training data; examples may include maximum mutual information (MMI), minimum classification error (MCE), and minimum phone error (MPE). Still referring to FIG.1, in some embodiments, an automatic speech recognition process may be said to decode speech (i.e., audible verbal content). Decoding of speech may occur when an automatic speech recognition system is presented with a new utterance and must compute a most likely sentence. In some cases, speech decoding may include a Viterbi algorithm. A Viterbi algorithm may include a dynamic programming algorithm for obtaining a maximum a posteriori probability estimate of a most likely sequence of hidden states (i.e., Viterbi path) that results in a sequence of observed events. Viterbi algorithms may be employed in context of Markov information sources and hidden Markov models. A Viterbi algorithm may be used to find a best 24 Attorney Docket No.1519-032PCT1 path, for example using a dynamically created combination hidden Markov model, having both acoustic and language model information, using a statically created combination hidden Markov model (e.g., finite state transducer [FST] approach). Still referring to FIG.1, in some embodiments, speech (i.e., audible verbal content) decoding may include considering a set of good candidates and not only a best candidate, when presented with a new utterance. In some cases, a better scoring function (i.e., re-scoring) may be used to rate each of a set of good candidates, allowing selection of a best candidate according to this refined score. In some cases, a set of candidates can be kept either as a list (i.e., N-best list approach) or as a subset of models (i.e., a lattice). In some cases, re-scoring may be performed by optimizing Bayes risk (or an approximation thereof). In some cases, re-scoring may include optimizing for sentence (including keywords) that minimizes an expectancy of a given loss function with regards to all possible transcriptions. For example, re-scoring may allow selection of a sentence that minimizes an average distance to other possible sentences weighted by their estimated probability. In some cases, an employed loss function may include Levenshtein distance, although different distance calculations may be performed, for instance for specific tasks. In some cases, a set of candidates may be pruned to maintain tractability. Still referring to FIG.1, in some embodiments, an automatic speech recognition process may employ dynamic time warping (DTW)-based approaches. Dynamic time warping may include algorithms for measuring similarity between two sequences, which may vary in time or speed. For instance, similarities in walking patterns would be detected, even if in one video the person was walking slowly and if in another he or she were walking more quickly, or even if there were accelerations and deceleration during the course of one observation. DTW has been applied to video, audio, and graphics – indeed, any data that can be turned into a linear representation can be analyzed with DTW. In some cases, DTW may be used by an automatic speech recognition process to cope with different speaking (i.e., audible verbal content) speeds. In some cases, DTW may allow computing device to find an optimal match between two given sequences (e.g., time series) with certain restrictions. That is, in some cases, sequences can be "warped" non-linearly to match each other. In some cases, a DTW-based sequence alignment method may be used in context of hidden Markov models. Still referring to FIG.1, in some embodiments, an automatic speech recognition process may include a neural network. Neural network may include any neural network, for example 25 Attorney Docket No.1519-032PCT1 those disclosed with reference to FIGS.2-4. In some cases, neural networks may be used for automatic speech recognition, including phoneme classification, phoneme classification through multi-objective evolutionary algorithms, isolated word recognition, audiovisual speech recognition, audiovisual speaker recognition and speaker adaptation. In some cases, neural networks employed in automatic speech recognition may make fewer explicit assumptions about feature statistical properties than HMMs and therefore may have several qualities making them attractive recognition models for speech recognition. When used to estimate the probabilities of a speech feature segment, neural networks may allow discriminative training in a natural and efficient manner. In some cases, neural networks may be used to effectively classify audible verbal content over short-time interval, for instance such as individual phonemes and isolated words. In some embodiments, a neural network may be employed by automatic speech recognition processes for pre-processing, feature transformation and / or dimensionality reduction, for example prior to HMM-based recognition. In some embodiments, long short-term memory (LSTM) and related recurrent neural networks (RNNs) and Time Delay Neural Networks (TDNN's) may be used for automatic speech recognition, for example over longer time intervals for continuous speech recognition. Still referring to FIG.1, in some embodiments, apparatus 100 captures a first image of slide 116 at a first position. In some embodiments, first image may be captured using at least an optical system 120. Still referring to FIG.1, in some embodiments, capturing a first image of a slide 116 at a first position may include using actuator mechanism 124 and / or actuator controls to move optical system 120 and / or slide 116 into desired positions. In some embodiments, first image includes an image of the entire sample and / or the entire slide 116. In some embodiments, first image includes an image of a region of a sample. In some embodiments, first image includes a wider angle image than second image (described below). In some embodiments, first image includes a lower resolution image than second image. Still referring to FIG.1, in some embodiments, a machine vision system and / or an optical character recognition system may be used to determine one or more features of sample and / or slide 116. Such feature determinations may be used to, for example, remove artifacts from images and / or annotate images as described below. In a non-limiting example, an optical character recognition system may be used to identify writing on slide 116, and this may be used 26 Attorney Docket No.1519-032PCT1 to annotate an image of slide 116. In another non-limiting example, a machine vision system may be used to detect dust particles in a slide 116, and an artifact removal machine learning model (described below) may remove the dust particles from the image. Still referring to FIG.1, in some embodiments, apparatus 100 may capture a plurality of images of the same X and Y coordinates at differing focus depths. As used herein, a “focus depth” is a depth within a sample that an optical system is in focus. As used herein, a “focus distance” is a distance from optical system at which optical sensor is in focus. In some embodiments, first image and second image may have different focus distances and / or focus depths. In some embodiments, such images may be stored as a plurality of layers of an image. For example, each layer may correspond with a particular focus depth. In some embodiments, a user may be able to navigate between layers using user interface 136. In a non-limiting example, this may be implemented by displaying to a user a focus depth selection menu. In this example, user may select a first focus depth, and an image taken at first focus depth may be displayed. In this example, user may then select a second focus depth, and an image taken at second focus depth may be displayed. In some embodiments, apparatus 100 may capture images at a particular focus depth upon selection of that focus depth by a user. Still referring to FIG.1, in some embodiments, apparatus 100 may include a machine vision system. In some embodiments, a machine vision system may include at least a camera. A machine vision system may use images, such as images from at least a camera, to make a determination about a scene, space, and / or object. For example, in some cases a machine vision system may be used for world modeling or registration of objects within a space. In some cases, registration may include image processing, such as without limitation object recognition, feature detection, edge / corner detection, and the like. Non-limiting example of feature detection may include scale invariant feature transform (SIFT), Canny edge detection, Shi Tomasi corner detection, and the like. In some cases, registration may include one or more transformations to orient a camera frame (or an image or video stream) relative a three-dimensional coordinate system; exemplary transformations include without limitation homography transforms and affine transforms. In an embodiment, registration of first frame to a coordinate system may be verified and / or corrected using object identification and / or computer vision, as described above. For instance, and without limitation, an initial registration to two dimensions, represented for instance as registration to the x and y coordinates, may be performed using a two-dimensional 27 Attorney Docket No.1519-032PCT1 projection of points in three dimensions onto a first frame, however. A third dimension of registration, representing depth and / or a z axis, may be detected by comparison of two frames; for instance, where first frame includes a pair of frames captured using a pair of cameras (e.g., stereoscopic camera also referred to in this disclosure as stereo-camera), image recognition and / or edge detection software may be used to detect a pair of stereoscopic views of images of an object; two stereoscopic views may be compared to derive z-axis values of points on object permitting, for instance, derivation of further z-axis points within and / or around the object using interpolation. This may be repeated with multiple objects in field of view, including without limitation environmental features of interest identified by object classifier and / or indicated by an operator. In an embodiment, x and y axes may be chosen to span a plane common to two cameras used for stereoscopic image capturing and / or an xy plane of a first frame; a result, x and y translational components and ϕ may be pre-populated in translational and rotational matrices, for affine transformation of coordinates of object, also as described above. Initial x and y coordinates and / or guesses at transformational matrices may alternatively or additionally be performed between first frame and second frame, as described above. For each point of a plurality of points on object and / or edge and / or edges of object as described above, x and y coordinates of a first stereoscopic frame may be populated, with an initial estimate of z coordinates based, for instance, on assumptions about object, such as an assumption that ground is substantially parallel to an xy plane as selected above. Z coordinates, and / or x, y, and z coordinates, registered using image capturing and / or object identification processes as described above may then be compared to coordinates predicted using initial guess at transformation matrices; an error function may be computed using by comparing the two sets of points, and new x, y, and / or z coordinates, may be iteratively estimated and compared until the error function drops below a threshold level. In some cases, a machine vision system may use a classifier, such as any classifier described throughout this disclosure. Still referring to FIG.1, in some embodiments, image data may be processed using optical character recognition. In some embodiments, optical character recognition or optical character reader (OCR) includes automatic conversion of images of written (e.g., typed, handwritten or printed text) into machine-encoded text. In some cases, recognition of at least a keyword from image data may include one or more processes, including without limitation optical character recognition (OCR), optical word recognition, intelligent character recognition, 28 Attorney Docket No.1519-032PCT1 intelligent word recognition, and the like. In some cases, OCR may recognize written text, one glyph or character at a time. In some cases, optical word recognition may recognize written text, one word at a time, for example, for languages that use a space as a word divider. In some cases, intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine-learning processes. In some cases, intelligent word recognition (IWR) may recognize written text, one word at a time, for instance by employing machine-learning processes. Still referring to FIG.1, in some cases OCR may be an "offline" process, which analyses a static document or image frame. In some cases, handwriting movement analysis can be used as input to handwriting recognition. For example, instead of merely using shapes of glyphs and words, this technique may capture motions, such as the order in which segments are drawn, the direction, and the pattern of putting the pen down and lifting it. This additional information may make handwriting recognition more accurate. In some cases, this technology may be referred to as “online” character recognition, dynamic character recognition, real-time character recognition, and intelligent character recognition. Still referring to FIG.1, in some cases, OCR processes may employ pre-processing of image data. Pre-processing process may include without limitation de-skew, de-speckle, binarization, line removal, layout analysis or “zoning,” line and word detection, script recognition, character isolation or “segmentation,” and normalization. In some cases, a de-skew process may include applying a transform (e.g., homography or affine transform) to image data to align text. In some cases, a de-speckle process may include removing positive and negative spots and / or smoothing edges. In some cases, a binarization process may include converting an image from color or greyscale to black-and-white (i.e., a binary image). Binarization may be performed as a simple way of separating text (or any other desired image component) from a background of image data. In some cases, binarization may be required for example if an employed OCR algorithm only works on binary images. In some cases, a line removal process may include removal of non-glyph or non-character imagery (e.g., boxes and lines). In some cases, a layout analysis or “zoning” process may identify columns, paragraphs, captions, and the like as distinct blocks. In some cases, a line and word detection process may establish a baseline for word and character shapes and separate words, if necessary. In some cases, a script recognition process may, for example in multilingual documents, identify script allowing an 29 Attorney Docket No.1519-032PCT1 appropriate OCR algorithm to be selected. In some cases, a character isolation or “segmentation” process may separate signal characters, for example character-based OCR algorithms. In some cases, a normalization process may normalize aspect ratio and / or scale of image data. Still referring to FIG.1, in some embodiments an OCR process may include an OCR algorithm. Exemplary OCR algorithms include matrix matching process and / or feature extraction processes. Matrix matching may involve comparing an image to a stored glyph on a pixel-by- pixel basis. In some case, matrix matching may also be known as “pattern matching,” “pattern recognition,” and / or “image correlation.” Matrix matching may rely on an input glyph being correctly isolated from the rest of image data. Matrix matching may also rely on a stored glyph being in a similar font and at a same scale as input glyph. Matrix matching may work best with typewritten text. Still referring to FIG.1, in some embodiments, an OCR process may include a feature extraction process. In some cases, feature extraction may decompose a glyph into at least a feature. Exemplary non-limiting features may include corners, edges, lines, closed loops, line direction, line intersections, and the like. In some cases, feature extraction may reduce dimensionality of representation and may make the recognition process computationally more efficient. In some cases, extracted feature may be compared with an abstract vector-like representation of a character, which might reduce to one or more glyph prototypes. General techniques of feature detection in computer vision are applicable to this type of OCR. In some embodiments, machine-learning processes like nearest neighbor classifiers (e.g., k-nearest neighbors algorithm) may be used to compare image features with stored glyph features and choose a nearest match. OCR may employ any machine-learning process described in this disclosure, for example machine-learning processes described with reference to FIGS.2-4. Exemplary non-limiting OCR software includes Cuneiform and Tesseract. Cuneiform is a multi- language, open-source optical character recognition system originally developed by Cognitive Technologies of Moscow, Russia. Tesseract is free OCR software originally developed by Hewlett-Packard of Palo Alto, California, United States. Still referring to FIG.1, in some cases, OCR may employ a two-pass approach to character recognition. A first pass may try to recognize a character. Each character that is satisfactory is passed to an adaptive classifier as training data. The adaptive classifier then gets a chance to recognize characters more accurately as it further analyzes image data. Since the 30 Attorney Docket No.1519-032PCT1 adaptive classifier may have learned something useful a little too late to recognize characters on the first pass, a second pass is run over the image data. Second pass may include adaptive recognition and use characters recognized with high confidence on the first pass to recognize better remaining characters on the second pass. In some cases, two-pass approach may be advantageous for unusual fonts or low-quality image data. Another exemplary OCR software tool include OCRopus. OCRopus development is led by German Research Centre for Artificial Intelligence in Kaiserslautern, Germany. In some cases, OCR software may employ neural networks. Still referring to FIG.1, in some cases, OCR may include post-processing. For example, OCR accuracy may be increased, in some cases, if output is constrained by a lexicon. A lexicon may include a list or set of words that are allowed to occur in a document. In some cases, a lexicon may include, for instance, all the words in the English language, or a more technical lexicon for a specific field. In some cases, an output stream may be a plain text stream or file of characters. In some cases, an OCR process may preserve an original layout of image data. In some cases, near-neighbor analysis can make use of co-occurrence frequencies to correct errors, by noting that certain words are often seen together. For example, “Washington, D.C.” is generally far more common in English than “Washington DOC.” In some cases, an OCR process may make us of a priori knowledge of grammar for a language being recognized. For example, grammar rules may be used to help determine if a word is likely to be a verb or a noun. Distance conceptualization may be employed for recognition and classification. For example, a Levenshtein distance algorithm may be used in OCR post-processing to further optimize results. Still referring to FIG.1, in some embodiments, apparatus 100 removes an artifact from an image. As used herein, an “artifact” is a visual inaccuracy, an element of an image that distracts from an element of interest, an element of an image that obscures an element of interest, or another undesirable element of an image. Still referring to FIG.1, apparatus 100 may include an image processing module. As used in this disclosure, an “image processing module” is a component designed to process digital images. In an embodiment, image processing module may include a plurality of software algorithms that can analyze, manipulate, or otherwise enhance an image, such as, without limitation, a plurality of image processing techniques as described below. In another embodiment, image processing module may slow include hardware components such as, without 31 Attorney Docket No.1519-032PCT1 limitation, one or more graphics processing units (GPUs) that can accelerate the processing of large amount of images. In some cases, image processing module may be implemented with one or more image processing libraries such as, without limitation, OpenCV, PIL / Pillow, ImageMagick, and the like. Still referring to FIG.1, image processing module may be configured to receive images from optical sensor 120. In a non-limiting example, image processing module may be configured to receive images by generating a first image capture parameter, transmitting a command to optical sensor 120 to take first image of a plurality of images with the first image capture parameter, generate a second image capture parameter, transmit a command to optical sensor 120 to take second image of a plurality of images with the second image capture parameter, and receive, from optical sensor 120, first image and second image. In another non-limiting example, plurality of images may be taken by optical sensor 120 using the same image capture parameter. Image capture parameter may be generated as a function of user input. Still referring to FIG.1, plurality of images may be transmitted, from optical sensor 120 to image processing module, via any suitable electronic communication protocol, including without limitation packet-based protocols such as transfer control protocol-internet protocol (TCP-IP), file transfer protocol (FTP) or the like. Receiving images may include retrieval of images from a data store containing images as described below; for instance, and without limitation, images may be retrieved using a query that specifies a timestamp that images may be required to match. Still referring to FIG.1, image processing module may be configured to process images. In an embodiment, image processing module may be configured to compress and / or encode images to reduce the file size and storage requirements while maintaining the essential visual information needed for further processing steps as described below. In an embodiment, compression and / or encoding of plurality of images may facilitate faster transmission of images. In some cases, image processing module may be configured to perform a lossless compression on images, wherein the lossless compression may maintain the original image quality of images. In a non-limiting example, image processing module may utilize one or more lossless compression algorithms, such as, without limitation, Huffman coding, Lempel-Ziv-Welch (LZW), Run-Length Encoding (RLE), and / or the like to identify and remove redundancy in each image in a plurality of images without losing any information. In such embodiment, compressing 32 Attorney Docket No.1519-032PCT1 and / or encoding each image of a plurality of images may include converting the file format of each image into PNG, GIF, lossless JPEG2000 or the like. In an embodiment, images compressed via lossless compression may be perfectly reconstructed to the original form (e.g., original image resolution, dimension, color representation, format, and the like) of images. In other cases, image processing module may be configured to perform a lossy compression on plurality of images, wherein the lossy compression may sacrifice some image quality of images to achieve higher compression ratios. In a non-limiting example, image processing module may utilize one or more lossy compression algorithms, such as, without limitation, Discrete Cosine Transform (DCT) in JPEG or Wavelet Transform in JPEG2000, discard some less significant information within images, resulting in a smaller file size but a slight loss of image quality of images. In such embodiment, compressing and / or encoding each image of a plurality of images may include converting the file format of each image into JPEG, WebP, lossy JPEG2000, or the like. Still referring to FIG.1, in an embodiment, processing images may include determining a degree of quality of depiction of a region of interest of an image or a plurality of images. In an embodiment, image processing module may determine a degree of blurriness of images. In a non-limiting example, image processing module may perform a blur detection by taking a Fourier transform, or an approximation such as a Fast Fourier Transform (FFT) of images and analyzing a distribution of low and high frequencies in the resulting frequency-domain depiction of images; for instance, and without limitation, numbers of high-frequency values below a threshold level may indicate blurriness. In another non-limiting example, detection of blurriness may be performed by convolving images, a channel of images, or the like with a Laplacian kernel; for instance, and without limitation, this may generate a numerical score reflecting a number of rapid changes in intensity shown in each image, such that a high score indicates clarity and a low score indicates blurriness. In some cases, blurriness detection may be performed using a Gradient-based operator, which measures operators based on the gradient or first derivative of images, based on the hypothesis that rapid changes indicate sharp edges in the image, and thus are indicative of a lower degree of blurriness. In some cases, blur detection may be performed using Wavelet-based operator, which takes advantage of the capability of coefficients of the discrete wavelet transform to describe the frequency and spatial content of images. In some cases, blur detection may be performed using statistics-based operators take 33 Attorney Docket No.1519-032PCT1 advantage of several image statistics as texture descriptors in order to compute a focus level. In other cases, blur detection may be performed by using discrete cosine transform (DCT) coefficients in order to compute a focus level of images from its frequency content. Additionally, or alternatively, image processing module may be configured to rank images according to degree of quality of depiction of a region of interest and select a highest-ranking image from a plurality of images. Still referring to FIG.1, processing images may include enhancing at least a region of interest via a plurality of image processing techniques to improve the quality (or degree of quality of depiction) of an image for better processing and analysis as described further in this disclosure. In an embodiment, image processing module may be configured to perform a noise reduction operation on an image, wherein the noise reduction operation may remove or minimize noise (arises from various sources, such as sensor limitations, poor lighting conditions, image compression, and / or the like), resulting in a cleaner and more visually coherent image. In some cases, noise reduction operation may be performed using one or more image filters; for instance, and without limitation, noise reduction operation may include Gaussian filtering, median filtering, bilateral filtering, and / or the like. Noise reduction operation may be done by image processing module, by averaging or filtering out pixel values in neighborhood of each pixel of an image to reduce random variations. Still referring to FIG.1, in another embodiment, image processing module may be configured to perform a contrast enhancement operation on an image. In some cases, an image may exhibit low contrast, which may, for example, make a feature difficult to distinguish from the background. Contrast enhancement operation may improve the contrast of an image by stretching the intensity range of the image and / or redistributing the intensity values (i.e., degree of brightness or darkness of a pixel in the image). In a non-limiting example, intensity value may represent the gray level or color of each pixel, scale from 0 to 255 in intensity range for an 8-bit image, and scale from 0 to 16,777,215 in a 24-bit color image. In some cases, contrast enhancement operation may include, without limitation, histogram equalization, adaptive histogram equalization (CLAHE), contrast stretching, and / or the like. Image processing module may be configured to adjust the brightness and darkness levels within an image to make a feature more distinguishable (i.e., increase degree of quality of depiction). Additionally, or alternatively, image processing module may be configured to perform a brightness normalization operation to 34 Attorney Docket No.1519-032PCT1 correct variations in lighting conditions (i.e., uneven brightness levels). In some cases, an image may include a consistent brightness level across a region after brightness normalization operation performed by image processing module. In a non-limiting example, image processing module may perform a global or local mean normalization, where the average intensity value of an entire image or region of an image may be calculated and used to adjust the brightness levels. Still referring to FIG.1, in other embodiments, image processing module may be configured to perform a color space conversion operation to increase degree of quality of depiction. In a non-limiting example, in case of a color image (i.e., RGB image), image processing module may be configured to convert RGB image to grayscale or HSV color space. Such conversion may emphasize the differences in intensity values between a region or feature of interest and the background. Image processing module may further be configured to perform an image sharpening operation such as, without limitation, unsharp masking, Laplacian sharpening, high-pass filtering, and / or the like. Image processing module may use image sharpening operation to enhance the edges and fine details related to a region or feature of interest within an image by emphasizing high-frequency components within an image. Still referring to FIG.1, processing images may include isolating a region or feature of interest from the rest of an image as a function of plurality of image processing techniques. Images may include highest-ranking image selected by image processing module as described above. In an embodiment, plurality of image processing techniques may include one or more morphological operations, wherein the morphological operations are techniques developed based on set theory, lattice theory, topology, and random functions used for processing geometrical structures using a structuring element. A “structuring element,” for the purpose of this disclosure, is a small matrix or kernel that defines a shape and size of a morphological operation. In some cases, structing element may be centered at each pixel of an image and used to determine an output pixel value for that location. In a non-limiting example, isolating a region or feature of interest from an image may include applying a dilation operation, wherein the dilation operation is a basic morphological operation configured to expand or grow the boundaries of objects (e.g., a cell, a dust particle, and the like) in an image. In another non-limiting example, isolating a region or feature of interest from an image may include applying an erosion operation, wherein the erosion operation is a basic morphological operation configured to shrink or erode the boundaries of objects in an image. In another non-limiting example, isolating a region or feature 35 Attorney Docket No.1519-032PCT1 of interest from an image may include applying an opening operation, wherein the opening operation is a basic morphological operation configured to remove small objects or thin structures from an image while preserving larger structures. In a further non-limiting example, isolating a region or feature of interest from an image may include applying a closing operation, wherein the closing operation is a basic morphological operation configured to fill in small gaps or holes in objects in an image while preserving the overall shape and size of the objects. These morphological operations may be performed by image processing module to enhance the edges of objects, remove noise, or fill gaps in a region or feature of interest before further processing. Still referring to FIG.1, in an embodiment, isolating a region or feature of interest from an image may include utilizing an edge detection technique, which may detect one or more shapes defined by edges. An “edge detection technique,” as used in this disclosure, includes a mathematical method that identifies points in a digital image, at which the image brightness changes sharply and / or has a discontinuity. In an embodiment, such points may be organized into straight and / or curved line segments, which may be referred to as “edges.” Edge detection technique may be performed by image processing module, using any suitable edge detection algorithm, including without limitation Canny edge detection, Sobel operator edge detection, Prewitt operator edge detection, Laplacian operator edge detection, and / or Differential edge detection. Edge detection technique may include phase congruency-based edge detection, which finds all locations of an image where all sinusoids in the frequency domain, for instance as generated using a Fourier decomposition, may have matching phases which may indicate a location of an edge. Edge detection technique may be used to detect a shape of a feature of interest such as a cell, indicating a cell membrane or wall; in an embodiment, edge detection technique may be used to find closed figures formed by edges. Still referring to FIG.1, in a non-limiting example, isolating a feature of interest from an image may include determining a feature of interest via edge detection technique. A feature of interest may include a specific area within a digital image that contains information relevant to further processing as described below. In a non-limiting example, an image data located outside a feature of interest may include irrelevant or extraneous information. Such portion of an image containing irrelevant or extraneous information may be disregarded by image processing module, thereby allowing resources to be concentrated at a feature of interest. In some cases, feature of interest may vary in size, shape, and / or location within an image. In a non-limiting example 36 Attorney Docket No.1519-032PCT1 feature of interest may be presented as a circle around the nucleus of a cell. In some cases, feature of interest may specify one or more coordinates, distances and the like, such as center and radius of a circle around the nucleus of a cell in an image. Image processing module may then be configured to isolate feature of interest from the image based on feature of interest. In a non- limiting example, image processing module may crop an image according to a bounding box around a feature of interest. Still referring to FIG.1, image processing module may be configured to perform a connected component analysis (CCA) on an image for feature of interest isolation. As used in this disclosure, a “connected component analysis (CCA),” also known as connected component labeling, is an image processing technique used to identify and label connected regions within a binary image (i.e., an image which each pixel having only two possible values: 0 or 1, black or white, or foreground and background). “Connected regions,” as described herein, is a group of adjacent pixels that share the same value and are connected based on a predefined neighborhood system such as, without limitation, 4-connected or 8-connected neighborhoods. In some cases, image processing module may convert an image into a binary image via a thresholding process, wherein the thresholding process may involve setting a threshold value that separates the pixels of an image corresponding to feature of interest (foreground) from those corresponding to the background. Pixels with intensity values above the threshold may be set to 1 (white) and those below the threshold may be set to 0 (black). In an embodiment, CCA may be employed to detect and extract feature of interest by identifying a plurality of connected regions that exhibit specific properties or characteristics of the feature of interest. Image processing module may then filter plurality of connected regions by analyzing plurality of connected regions properties such as, without limitation, area, aspect ratio, height, width, perimeter, and / or the like. In a non-limiting example, connected components that closely resemble the dimensions and aspect ratio of feature of interest may be retained, by image processing module as feature of interest, while other components may be discarded. Image processing module may be further configured to extract feature of interest from an image for further processing as described below. Still referring to FIG.1, in an embodiment, isolating feature of interest from an image may include segmenting a region depicting a feature of interest into a plurality sub-regions. Segmenting a region into sub-regions may include segmenting a region as a function of feature of interest and / or CCA via an image segmentation process. As used in this disclosure, an “image 37 Attorney Docket No.1519-032PCT1 segmentation process” is a process for partition a digital image into one or more segments, where each segment represents a distinct part of the image. Image segmentation process may change the representation of images. Image segmentation process may be performed by image processing module. In a non-limiting example, image processing module may perform a region- based segmentation, wherein the region-based segmentation involves growing regions from one or more seed points or pixels on an image based on a similarity criterion. Similarity criterion may include, without limitation, color, intensity, texture, and / or the like. In a non-limiting example, region-based segmentation may include region growing, region merging, watershed algorithms, and the like. Still referring to FIG.1, in some embodiments, apparatus 100 may remove an artifact identified by a machine vision system or an optical character recognition system, which are described above. Non-limiting examples of artifacts that may be removed include dust particles, bubbles, cracks in slide 116, writing on slide 116, shadows, visual noise such as in a grainy image, and the like. In some embodiments, an artifact may be partially removed and / or lowered in visibility. Still referring to FIG.1, in some embodiments, an artifact may be removed using an artifact removal machine learning model 140. In some embodiments, artifact removal machine learning model 140 may be trained on a dataset including images, associated with images without artifacts. In some embodiments, artifact removal machine learning model 140 may accept as an input an image including an artifact and may output an image without the artifact. For example, artifact removal machine learning model 140 may accept as an input an image including a bubble in a slide and may output an image that does not include the bubble. In some embodiments, artifact removal machine learning model 140 may include a generative machine learning model such as a diffusion model. A diffusion model may learn the structure of a dataset by modeling the way data points diffuse through a latent space. In some embodiments, artifact removal may be done locally. For example, apparatus 100 may include an already trained artifact removal machine learning model and may apply the model to an image. In some embodiments, artifact removal may be done externally. For example, apparatus 100 may transmit image data to another computing device and may receive an image with an artifact removed. In some embodiments, an artifact may be removed in real time. In some embodiments, an artifact may be 38 Attorney Docket No.1519-032PCT1 removed based on identification by a user. For example, a user may drag a box around an artifact using a mouse cursor, and apparatus 100 may remove an artifact in the box. Still referring to FIG.1, in some embodiments, apparatus 100 may remove an artifact from first image. In some embodiments, apparatus 100 may remove an artifact from second image. In some embodiments, apparatus 100 may remove an artifact from hybrid image. Still referring to FIG.1, in some embodiments, apparatus 100 displays first image to a user. In some embodiments, first image may be displayed to a user in real time. In some embodiments, first image may be displayed to a user using output interface 132. For example, first image may be displayed to a user a display such as a screen. In some embodiments, first image may be displayed to a user in the context of a graphical user interface (GUI). For example, a GUI may include controls for navigating an image such as controls for zooming in or out or changing where is being viewed. A GUI may include a touchscreen. Still referring to FIG.1, in some embodiments, apparatus 100 may receive a parameter set from a user. As used herein, a “parameter set” is a set of values that identify how an image is to be captured. A parameter set may be implemented as a data structure as described below. In some embodiments, apparatus 100 may receive a parameter set from a user using input interface 128. A parameter set may include X and Y coordinates indicating where the user wishes to view. A parameter set may include a desired level of zoom. As used herein, a “level of zoom” is a datum describing how zoomed in or out an image is to be captured at. A level of zoom may account for optical zoom and / or digital zoom. As a non-limiting example, a level of zoom may be “8x” zoom. A parameter set may include a desired focus depth. In a non-limiting example, user may manipulate input interface 128 such that a parameter set includes X and Y coordinates and a level of zoom corresponding to a more zoomed in view of a particular region of a sample. In some embodiments, a parameter set corresponds to a more zoomed in view of a particular region of a sample that is included in first image. This may be done, for example, to get a more detailed view of a small object. As used herein, unless indicated otherwise, an “X coordinate” and a “Y coordinate” refer to coordinates along perpendicular axes, where the plane defined by these axes is parallel to a plane of a surface of slide 116. In some cases, setting a zoom may include changing one or more optical elements within optical system. For example, setting a zoom may include replacing a first objective lens for a second objective lens having a different magnification. Additionally or alternative, one or more optical components "down beam" from 39 Attorney Docket No.1519-032PCT1 objective lens may be replaced to change a total magnification of optical system and, thereby, set a zoom. In some cases, setting a zoom may include changing a digital magnification. Digital magnification may include outputting an image, using output interface, at a different resolution, i.e. after re-scaling the image. Still referring to FIG.1, in some embodiments, apparatus 100 moves an item selected from the list consisting of slide port 144, slide 116, and at least an optical system 120 into a second position, wherein the location of the second position is based on the parameter set. Second position may be determined by, for example, modifying the position of optical system 120 relative to slide 116 based on parameter set. For example, parameter set may indicate that second position is achieved by modifying an X coordinate by 5mm in a particular direction. In this example, second position may be found by modifying optical system’s original position by 5mm in that direction. In some embodiments, such movement may be done using actuator mechanism 124. In some embodiments, actuator mechanism 124 may move slide port 144 such that slide 116 is in a position relative to at least an optical system 120 such that optical sensor 120 may capture an image as directed by parameter set. For example, slide 116 may rest on slide port 144 and movement of slide port 144 may move slide 116 as well. In some embodiments, actuator mechanism 124 may move slide 116 such that slide 116 is in a position relative to at least an optical system 120 such that optical sensor 120 may capture an image as directed by parameter set. For example, slide 116 may be connected to actuator mechanism 124 such that actuator mechanism 124 may move slide 116 relative to at least an optical system 120. In some embodiments, actuator mechanism 124 may move at least an optical system 120 such that slide 116 is in a position relative to slide 116 such that optical system 120 may capture an image as directed by parameter set. For example, slide 116 may be stationary, and actuator mechanism 124 may move at least an optical system 120 into position relative to slide 116. In some embodiments, actuator mechanism 124 may move more than one of slide port 144, slide 116, and at least an optical system 120 such that they are in the correct relative positions. In some embodiments, actuator mechanism 124 may move slide port 144, slide 116, and / or at least an optical system 120 in real time. For example, user input of a parameter set may cause a substantially immediate movement of items by actuator mechanism 124. Still referring to FIG.1, in some embodiments, apparatus 100 captures a second image of slide 116 at second position. In some embodiments, apparatus 100 may capture second image 40 Attorney Docket No.1519-032PCT1 using at least an optical system 120. In some embodiments, second image may include an image of a region of a sample. In some embodiments, second image may include an image of a region of an area captured in first image. For example, second image may include a more zoomed in, higher resolution per unit area, image of a region within first image. This may cause display of second image to allow a user to detect smaller details within the imaged region. Still referring to FIG.1, in some embodiments, second image includes a shift in X and Y coordinates relative to first image. For example, second image may partially overlap with first image. Still referring to FIG.1, in some embodiments, second image may include a different focus depth than first image. For example, if a region of interest of first image is out of focus, then second image may use an auto focus system and / or a focus depth set in parameter set to capture second image, where second image is in focus at the region of interest. In a non-limiting example, capturing second image may include detecting a height of a sample at a location of interest, and setting a focus height of the at least an optical system 120 based on the sample height at the region of interest. Still referring to FIG.1, in some embodiments, apparatus 100 may capture second image in real time. For example, user may manipulate input interface 128, creating parameter set, then actuator mechanism 124 may cause movement of slide 116 relative to optical system 120 to start substantially immediately after the input interface 128 was manipulated, then optical system 120 may capture second image substantially immediately after actuator mechanism 124 completes its movement. Real-time actuator mechanism 124 movement and image capture may allow user to choose a specific region of interest and receive high resolution image of that region in real time, without having to wait for detailed images of the entire slide 116 to be captured. In some embodiments, artifacts may also be removed in real time. In some embodiments, image may also be annotated in real time. In a non-limiting example, a user may use a mouse cursor to hover over a position in first image and may scroll the mouse wheel indicating that user wishes to zoom in on that location. In this example, X coordinate and Y coordinate of parameter set may be determined based on position of the cursor, and level of zoom of parameter set may be determined based on the amount that the user scrolled the mouse wheel. In this example, apparatus 100 may capture second image based on that parameter set and may apply processes described herein to remove artifacts from second image and annotate second image. In this 41 Attorney Docket No.1519-032PCT1 example, second image may then be displayed to user. While such a process may be performed quickly, instant feedback may be displayed to user as second image is being captured and modified. In a non-limiting example, a zoomed in version of first image may be displayed while second image is being captured, and a non-modified version of second image may be displayed while second image is being modified. Still referring to FIG.1, this process may be useful, for example, if a user is looking at an image of slide 116, finds a region of interest and wishes to zoom in on that region more than the resolution of the image would normally allow. Using this process, the user may adjust parameter set to zoom in and apparatus 100 may capture a higher resolution image, remove artifacts and display the image. This may be preferable over manually repeating a process of loading slide 116, setting parameters (to a higher resolution and / or zoom), and waiting for a device to generate and clean up an image. Still referring to FIG.1, in some embodiments, apparatus 100 displays second image to user. In some embodiments, second image may be displayed to user using output interface 132 or output device. Second image may be displayed as described above with respect to output device and display of first image. In some embodiments, displaying the second image to the user may include replacing a region of first image with second image to create a hybrid image; and displaying the hybrid image to the user. As used herein, a “hybrid image” is an image constructed by combining a first image with a second image. In some embodiments, creation of a hybrid image in this way may preserve second image. For example, if second image covers a smaller area at a higher resolution per unit area than first image, then second image may replace a lower resolution per unit area segment of first area corresponding to the region covered by second image. In some embodiments, image adjustments may be made to offset visual differences between first image and second image at a border of first image and second image in hybrid image. In a non-limiting example, color may be adjusted such that background color of the images is consistent along the border of the images. As another non-limiting example, brightness of the images may be adjusted such that there is no stark difference between brightness of the images. In some embodiments, artifacts may be removed from second image and / or hybrid image as described above. In some embodiments, second image may be displayed to user in real time. For example, adjustments (such as annotations and / or artifact removal may be started substantially immediately after second image is captured, and an adjusted version of 42 Attorney Docket No.1519-032PCT1 second image may be displayed to user substantially immediately after adjustments are done. In some embodiments, an unadjusted version of second image may be displayed to user while adjustments are being made. In some embodiments, where there are a plurality of images covering a specific region, when user zooms out using user interface 136, a lower resolution image of the region may be displayed. Still referring to FIG.1, in some embodiments, apparatus 100 may transmit first image, second image, hybrid image, and / or a data structure including a plurality of images to an external device. Such an external device may include, in non-limiting examples, a phone, tablet, or computer. In some embodiments, such a transmission may configure the external device to display an image. Still referring to FIG.1, in some embodiments, apparatus 100 may annotate an image. In some embodiments, apparatus 100 may annotate first image. In some embodiments, apparatus 100 may annotate second image. In some embodiments, apparatus 100 may annotate hybrid image. For example, upon creation of hybrid image, apparatus 100 may recognize a cell depicted in hybrid image as a cell of a particular type and may annotate hybrid image indicating the cell type. In some embodiments, apparatus 100 may annotate an image selected from the list consisting of first image, second image and hybrid image. Still referring to FIG.1, in some embodiments, annotations may be made as a function of user input of an annotation instruction into input interface 128. As used herein, an “annotation instruction” is a datum generated based on user input indicating whether to create an annotation or describing an annotation to be made. For example, user may select an option controlling whether apparatus 100 annotates images. In another example, user may manually annotate an image. In some embodiments, annotations may be made automatically. In some embodiments, annotations may be made using an annotation machine learning model. In some embodiments, annotation machine learning model may include an optical character recognition model, as described above. In some embodiments, annotation machine learning model may be trained using a dataset including image data, associated with text depicted by the image. In some embodiments, annotation machine learning model may accept as an input image data and may output annotated image data and / or annotations to apply to the image data. In some embodiments, annotation machine learning model may be used to convert text written on slides 116 to annotations on images. In some embodiments, annotation machine learning model may 43 Attorney Docket No.1519-032PCT1 include a machine vision model, as described above. In some embodiments, annotation machine learning model including a machine vision model may be trained on a data set including image data, associated with annotations indicating features of the image data. In some embodiments, annotation machine learning model may accept as an input image data and may output annotated image data and / or annotations to apply to image data. Non-limiting examples of features annotation machine learning model may be trained to recognize include cell types, features of cells, and objects in a slide 116 such as bubbles. In some embodiments, images may be annotated in real time. For example, annotation may start substantially immediately after an image is captured and / or a command to annotate an image is received, and annotated image may be displayed to user substantially immediately after annotation is completed. Still referring to FIG.1, in some embodiments, apparatus 100 may determine a visual element data structure. In some embodiments, apparatus 100 may display to a user a visual element as a function of visual element data structure. As used herein, a “visual element data structure” is a data structure describing a visual element. As non-limiting examples, visual elements may include first image, second image, hybrid image, and elements of a GUI. Still referring to FIG.1, in some embodiments, a visual element data structure may include a visual element. As used herein, a “visual element” is a datum that is displayed visually to a user. In some embodiments, a visual element data structure may include a rule for displaying visual element. In some embodiments, a visual element data structure may be determined as a function of first image, second image, and / or hybrid image. In some embodiments, a visual element data structure may be determined as a function of an item from the list consisting of first image, second image, hybrid image, a GUI element, and an annotation. In a non-limiting example, a visual element data structure may be generated such that visual element describing or a feature of first image, such as an annotation, is displayed to a user. Still referring to FIG.1, in some embodiments, visual element may include one or more elements of text, images, shapes, charts, particle effects, interactable features, and the like. As a non-limiting example, a visual element may include a touch screen button for setting zoom level. Still referring to FIG.1, a visual element data structure may include rules governing if or when visual element is displayed. In a non-limiting example, a visual element data structure may include a rule causing a visual element including an annotation describing first image, second 44 Attorney Docket No.1519-032PCT1 image, and / or hybrid image to be displayed when a user selects a specific region of first image, second image, and / or hybrid image using a GUI. Still referring to FIG.1, a visual element data structure may include rules for presenting more than one visual element, or more than one visual element at a time. In an embodiment, about 1, 2, 3, 4, 5, 10, 20, or 50 visual elements are displayed simultaneously. For example, a plurality of annotations may be displayed simultaneously. Still referring to FIG.1, in some embodiments, apparatus 100 may transmit visual element to a display such as a display include in output interface 132. A display may communicate visual element to user. A display may include, for example, a smartphone screen, a computer screen, or a tablet screen. A display may be configured to provide a visual interface. A visual interface may include one or more virtual interactive elements such as, without limitation, buttons, menus, and the like. A display may include one or more physical interactive elements, such as buttons, a computer mouse, or a touchscreen, that allow user to input data into the display. Interactive elements may be configured to enable interaction between a user and a computing device. In some embodiments, a visual element data structure is determined as a function of data input by user into a display. Still referring to FIG.1, a variable and / or datum described herein may be represented as a data structure. In some embodiments, a data structure may include one or more functions and / or variables, as a class might in object-oriented programming. In some embodiments, a data structure may include data in the form of a Boolean, integer, float, string, date, and the like. In a non-limiting example, an annotation data structure may include a string value representing text of the annotation. In some embodiments, data in a data structure may be organized in a linked list, tree, array, matrix, tenser, and the like. In a non-limiting example, annotation data structures may be organized in an array. In some embodiments, a data structure may include or be associated with one or more elements of metadata. A data structure may include one or more self-referencing data elements, which processor 104 may use in interpreting the data structure. In a non-limiting example, a data structure may include “<date>” and “< / date>,” tags, indicating that the content between the tags is a date. Still referring to FIG.1, a data structure may be stored in, for example, memory 108 or a database. Database may be implemented, without limitation, as a relational database, a key-value retrieval database such as a NOSQL database, or any other format or structure for use as a 45 Attorney Docket No.1519-032PCT1 database that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Database may alternatively or additionally be implemented using a distributed data storage protocol and / or data structure, such as a distributed hash table or the like. Database may include a plurality of data entries and / or records as described above. Data entries in a database may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and / or in linked tables such as tables related by one or more indices in a relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a database may store, retrieve, organize, and / or reflect data and / or records as used herein, as well as categories and / or populations of data consistently with this disclosure. Still referring to FIG.1, in some embodiments, a data structure may be read and / or manipulated by processor 104. In a non-limiting example, an image data structure may be read and displayed to user. In another non-limiting example, an image data structure may be modified to remove an artifact, as described above. Still referring to FIG.1, in some embodiments, a data structure may be calibrated. In some embodiments, a data structure may be trained using a machine learning algorithm. In a non-limiting example, a data structure may include an array of data representing the biases of connections of a neural network. In this example, the neural network may be trained on a set of training data, and a back propagation algorithm may be used to modify the data in the array. Machine learning models and neural networks are described further herein. Referring now to FIG.2, an exemplary embodiment of a machine-learning module 200 that may perform one or more machine-learning processes as described in this disclosure is illustrated. Machine-learning module may perform determinations, classification, and / or analysis steps, methods, processes, or the like as described in this disclosure using machine learning processes. A “machine learning process,” as used in this disclosure, is a process that automatedly uses training data 204 to generate an algorithm that will be performed by a computing device / module to produce outputs 208 given data provided as inputs 212; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. Still referring to FIG.2, “training data,” as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories 46 Attorney Docket No.1519-032PCT1 of data elements. For instance, and without limitation, training data 204 may include a plurality of data entries, each entry representing a set of data elements that were recorded, received, and / or generated together; data elements may be correlated by shared existence in a given data entry, by proximity in a given data entry, or the like. Multiple data entries in training data 204 may evince one or more trends in correlations between categories of data elements; for instance, and without limitation, a higher value of a first data element belonging to a first category of data element may tend to correlate to a higher value of a second data element belonging to a second category of data element, indicating a possible proportional or other mathematical relationship linking values belonging to the two categories. Multiple categories of data elements may be related in training data 204 according to various correlations; correlations may indicate causative and / or predictive links between categories of data elements, which may be modeled as relationships such as mathematical relationships by machine-learning processes as described in further detail below. Training data 204 may be formatted and / or organized by categories of data elements, for instance by associating data elements with one or more descriptors corresponding to categories of data elements. As a non-limiting example, training data 204 may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data 204 may be linked to descriptors of categories by tags, tokens, or other data elements; for instance, and without limitation, training data 204 may be provided in fixed-length formats, formats linking positions of data to categories such as comma-separated value (CSV) formats and / or self- describing formats such as extensible markup language (XML), JavaScript Object Notation (JSON), or the like, enabling processes or devices to detect categories of data. Alternatively or additionally, and continuing to refer to FIG.2, training data 204 may include one or more elements that are not categorized; that is, training data 204 may not be formatted or contain descriptors for some elements of data. Machine-learning algorithms and / or other processes may sort training data 204 according to one or more categorizations using, for instance, natural language processing algorithms, tokenization, detection of correlated values in raw data and the like; categories may be generated using correlation and / or other processing algorithms. As a non-limiting example, in a corpus of text, phrases making up a number “n” of compound words, such as nouns modified by other nouns, may be identified according to a statistically significant prevalence of n-grams containing such words in a particular order; such 47 Attorney Docket No.1519-032PCT1 an n-gram may be categorized as an element of language such as a “word” to be tracked similarly to single words, generating a new category as a result of statistical analysis. Similarly, in a data entry including some textual data, a person’s name may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine- learning algorithms, and / or automated association of data in the data entry with descriptors or into a given format. The ability to categorize data entries automatedly may enable the same training data 204 to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data 204 used by machine-learning module 200 may correlate any input data as described in this disclosure to any output data as described in this disclosure. As a non-limiting illustrative example, inputs may include image data and outputs may include annotations. Further referring to FIG.2, training data may be filtered, sorted, and / or selected using one or more supervised and / or unsupervised machine-learning processes and / or models as described in further detail below; such models may include without limitation a training data classifier 216. Training data classifier 216 may include a “classifier,” which as used in this disclosure is a machine-learning model as defined below, such as a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and / or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. A distance metric may include any norm, such as, without limitation, a Pythagorean norm. Machine-learning module 200 may generate a classifier using a classification algorithm, defined as a processes whereby a computing device and / or any module and / or component operating thereon derives a classifier from training data 204. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and / or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher’s linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and / or neural network-based classifiers. As a non- limiting example, training data classifier 216 may classify elements of training data to characters or numbers as in an optical character recognition model. 48 Attorney Docket No.1519-032PCT1 Still referring to FIG.2, machine-learning module 200 may be configured to perform a lazy-learning process 220 and / or protocol, which may alternatively be referred to as a “lazy loading” or “call-when-needed” process and / or protocol, may be a process whereby machine learning is conducted upon receipt of an input to be converted to an output, by combining the input and training set to derive the algorithm to be used to produce the output on demand. For instance, an initial set of simulations may be performed to cover an initial heuristic and / or “first guess” at an output and / or relationship. As a non-limiting example, an initial heuristic may include a ranking of associations between inputs and elements of training data 204. Heuristic may include selecting some number of highest-ranking associations and / or training data 204 elements. Lazy learning may implement any suitable lazy learning algorithm, including without limitation a K-nearest neighbors algorithm, a lazy naïve Bayes algorithm, or the like; persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various lazy- learning algorithms that may be applied to generate outputs as described in this disclosure, including without limitation lazy learning applications of machine-learning algorithms as described in further detail below. Alternatively or additionally, and with continued reference to FIG.2, machine-learning processes as described in this disclosure may be used to generate machine-learning models 224. A “machine-learning model,” as used in this disclosure, is a mathematical and / or algorithmic representation of a relationship between inputs and outputs, as generated using any machine- learning process including without limitation any process as described above, and stored in memory; an input is submitted to a machine-learning model 224 once created, which generates an output based on the relationship that was derived. For instance, and without limitation, a linear regression model, generated using a linear regression algorithm, may compute a linear combination of input data using coefficients derived during machine-learning processes to calculate an output datum. As a further non-limiting example, a machine-learning model 224 may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of "training" the network, in which elements from a training data 204 set are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers 49 Attorney Docket No.1519-032PCT1 of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning. Still referring to FIG.2, machine-learning algorithms may include at least a supervised machine-learning process 228. At least a supervised machine-learning process 228, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include image data as described above as inputs, image data with artifacts removed as outputs, and a scoring function representing a desired form of relationship to be detected between inputs and outputs; scoring function may, for instance, seek to maximize the probability that a given input and / or combination of elements inputs is associated with a given output to minimize the probability that a given input is not associated with a given output. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data 204. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various possible variations of at least a supervised machine-learning process 228 that may be used to determine relation between inputs and outputs. Supervised machine-learning processes may include classification algorithms as defined above. Further referring to FIG.2, machine learning processes may include at least an unsupervised machine-learning processes 232. An unsupervised machine-learning process, as used herein, is a process that derives inferences in datasets without regard to labels; as a result, an unsupervised machine-learning process may be free to discover any structure, relationship, and / or correlation provided in the data. Unsupervised processes may not require a response variable; unsupervised processes may be used to find interesting patterns and / or inferences between variables, to determine a degree of correlation between two or more variables, or the like. Still referring to FIG.2, machine-learning module 200 may be designed and configured to create a machine-learning model 224 using techniques for development of linear regression models. Linear regression models may include ordinary least squares regression, which aims to 50 Attorney Docket No.1519-032PCT1 minimize the square of the difference between predicted outcomes and actual outcomes according to an appropriate norm for measuring such a difference (e.g. a vector-space distance norm); coefficients of the resulting linear equation may be modified to improve minimization. Linear regression models may include ridge regression methods, where the function to be minimized includes the least-squares function plus term multiplying the square of each coefficient by a scalar amount to penalize large coefficients. Linear regression models may include least absolute shrinkage and selection operator (LASSO) models, in which ridge regression is combined with multiplying the least-squares term by a factor of 1 divided by double the number of samples. Linear regression models may include a multi-task lasso model wherein the norm applied in the least-squares term of the lasso model is the Frobenius norm amounting to the square root of the sum of squares of all terms. Linear regression models may include the elastic net model, a multi-task elastic net model, a least angle regression model, a LARS lasso model, an orthogonal matching pursuit model, a Bayesian regression model, a logistic regression model, a stochastic gradient descent model, a perceptron model, a passive aggressive algorithm, a robustness regression model, a Huber regression model, or any other suitable model that may occur to persons skilled in the art upon reviewing the entirety of this disclosure. Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher-order equation) providing a best predicted output / actual output fit is sought; similar methods to those described above may be applied to minimize error functions, as will be apparent to persons skilled in the art upon reviewing the entirety of this disclosure. Continuing to refer to FIG.2, machine-learning algorithms may include, without limitation, linear discriminant analysis. Machine-learning algorithm may include quadratic discriminant analysis. Machine-learning algorithms may include kernel ridge regression. Machine-learning algorithms may include support vector machines, including without limitation support vector classification-based regression processes. Machine-learning algorithms may include stochastic gradient descent algorithms, including classification and regression algorithms based on stochastic gradient descent. Machine-learning algorithms may include nearest neighbors algorithms. Machine-learning algorithms may include various forms of latent space regularization such as variational regularization. Machine-learning algorithms may include Gaussian processes such as Gaussian Process Regression. Machine-learning algorithms may 51 Attorney Docket No.1519-032PCT1 include cross-decomposition algorithms, including partial least squares and / or canonical correlation analysis. Machine-learning algorithms may include naïve Bayes methods. Machine- learning algorithms may include algorithms based on decision trees, such as decision tree classification or regression algorithms. Machine-learning algorithms may include ensemble methods such as bagging meta-estimator, forest of randomized trees, AdaBoost, gradient tree boosting, and / or voting classifier methods. Machine-learning algorithms may include neural net algorithms, including convolutional neural net processes. With continued reference to FIG.2, machine learning process may include a generative machine learning process. As used in this disclosure, a “generative machine learning process” is a process that automatedly, using a prompt (i.e., input), generates an output consistent with training data; this is in contrast to a non-machine learning software program where outputs are determined in advance by a user and written in a programming language. Usually generative machine learning processes determine patterns and structures from training data and use these patterns and structures to synthesize new data with similar characteristics, as a function of an input. With continued reference to FIG.2, generative machine learning processes may synthesize data of different types or domains, including without limitation text, code, images, molecules, audio (e.g., music), video, and robot actions (e.g., electromechanical system actions). Exemplary generative machine learning systems trained on words or word tokens, operant in text domain, include GPT-3, LaMDA, LLaMA, BLOOM, GPT-4, and the like. Exemplary machine learning processes trained on programming language text (i.e., code) include without limitation OpenAI Codex. Exemplary machine learning processes trained on sets of images (for instance with text captions) include Imagen, DALL-E, Midjourney, Adobe Firefly, Stable Diffusion, and the like; image generative machine learning processes, in some cases, may be trained for text-to- image generation and / or neural style transfer. Exemplary generative machine learning processes trained on molecular data include, without limitation, AlphaFold, which may be used for protein structure prediction and drug discovery. Generative machine learning processes trained on audio training data include MusicLM which may be trained on audio waveforms of music correlated with text annotations; music generative machine learning processes, in some cases, may generate new musical samples based on text descriptions. Exemplary generative machine learning processes trained on video include without limitation RunwayML and Make-A-Video by Meta 52 Attorney Docket No.1519-032PCT1 Platforms. Finally, exemplary generative machine learning processes trained using robotic action data include without limitation UniPi from Google Research. With continued reference to FIG.2, in some cases a generative machine learning process may include a generative adversarial network. As used in this disclosure, a “generative adversarial network (GAN)” is a machine learning process that includes at least two adverse networks configured to synthesize data according to prescribed rules (e.g., rules of a game). In some cases, a generative adversarial network may include a generative and a discriminative network. An exemplary GAN may be described according to a following game: Each probability space (Ω, µref) defines a GAN game. There are two adverse networks: a generator network and a discriminator network. Generator network strategy set is P(Ω), the set of all probability measures µG on Ω. Discriminator network strategy set is the set of Markov kernels µD : Ω → P[0,1], where P[0,1] is set of probability measures on [0,1]. GAN game may be a zero-sum game, with objective function: ^^^ ^^ீ, ^^^^ ≔ ^^௫~ఓ^^^,௬~ఓವ^௫^^ln y^ ^ ^^௫~ఓಸ,௬~ఓವ^௫^^ln^1 െ y^^ and discriminator network may aim to maximize the objective. Specifically, generator network seeks to approach µG ≈ µref; said another way, generator network produces data that matches its own output distribution as closely as possible to a reference distribution (provided with training data). Discriminator network outputs a value close to 1 when the data appears to be from reference (training data) distribution, and to output a value close to 0 when the data looks like it came from generator network distribution. Generally speaking, generative network generates data while discriminative network evaluates it, with contest operating in terms of data distributions. In some embodiments, generator network may learn to map from a latent space to a data distribution of interest, while discriminator network may distinguish data produced by the generator network from a true data distribution (e.g., training data). In some cases, generator network's training objective is to increase an error rate of discriminator network (i.e., “fool” the discriminator network by producing novel data that the discriminator thinks are not synthesized but, instead, are part of training data). In some cases, a known dataset may serve as initial training data for discriminator network. Training may involve presenting discriminator network with samples from training dataset until it achieves acceptable accuracy. In some cases, generator network may be trained on 53 Attorney Docket No.1519-032PCT1 whether the generator network succeeds in fooling discriminator network. A generator network may be seeded with randomized input that is sampled from a predefined latent space (e.g. a multivariate normal distribution). Thereafter, data synthesized by generator network may be evaluated by discriminator network. Independent backpropagation procedures may be applied to both networks so that generator network may produce better samples, while discriminator network may become more skilled at flagging synthetic samples. When used for image generation, generator network may be a deconvolutional neural network, and discriminator may be a convolutional neural network. Still referring to FIG.2, in some embodiments generative machine learning process may include a generative adversarial network (GAN), such as, without limitation, CycleGAN. CycleGAN may use a pair of inter-reliant neural network generators relying on the outputs of each other to be used as inputs. A CycleGAN process enables a forward and backward translation between domains to occur simultaneously. CycleGAN processes may include a set of calculations. CycleGAN may differ from paired training data, wherein paired training data consists of training examples ^ ^^^, ^^^^^ୀே^ , where the correspondence between ^^^and ^^^already exists. In unpaired training data, as applied to CycleGAN processes, a source set ^ ^^^^^ୀே^ ^ ^^^∈ ^^^ and a target set ^ ^^^^^ெୀ^^ ^^^∈ ^^^ , with no information provided as to which ^^^which ^^^. In the case of unpaired data, starting with a first dataset in domain X and a second dataset in domain Y, a general mapping may be enabled such that ^^ ∶ ^^ → ^^, wherein the output ^^^ = G(x), x ∈ X and is indistinguishable from dataset y ∈ Y by an adversary trained to classify ^^^ apart from y. CycleGAN processes may include a translator operating in a reverse direction from a separate translator which should produce exactly inverse results such that G : X → Y and F : Y → X, then G and F should be inverses of each other. ^^௫operates as a distinguisher between datasets {x} and translated datasets {F(y)}. Conversely ^^௬operates as a discriminator between {y} and {G(x)}. Next adversarial loss is applied to both mapping functions such that: ʆୋ^^( ^^, ^^^, ^^, ^^) = ^^௬~^^ೌ^ೌ^௬^^log ^^௬^ ^^^^ ^ ^^௫~^^ೌ^ೌ^௫^^log^1 െ ^^௬^ ^^^ ^^^^^ consistency loss such that: ʆ^௬^(G, F) = ^^௫~^^ೌ^ೌ^௫^^|| ^^൫ ^^^^^^൯ െ ^^||^^ ^ ^^௬~^^ೌ^ೌ^௬^^|| ^^൫ ^^^^^^൯ െ ^^||^^.The 54 Attorney Docket No.1519-032PCT1 ʆ (G, F, ^^^, ^^^^ ൌ ʆୋ^^^ ^^, ^^^, ^^, ^^^ ^ ʆୋ^^(F, ^^^, ^^, ^^^ ^ ^^ʆୡ^ୡ(G, F), where ^^ controls the relative importance of the objectives. Still referring to FIG.2, in a separate non-limiting embodiment, generative machine learning model may include diffusion process and / or diffusion model. An exemplary diffusion model may include an energy-guided stochastic differential equation (EGSDE) process. EGSDE relies on a score-based diffusion model to translate data from one domain to another. Contrary to the CycleGAN process explained above, EGSDE’s use score-based diffusion models (SBDMs) to perturb an initial dataset to a Gaussian noise then reverse the process to transform the noise back to the data distribution. This diffusion model adopts a pretrained energy function based on data from the initial source domain and data from the final target domain to guide the inference process of a pretrained stochastic differential equation (SDE). As used herein, “energy function” is defined as a formulaic approximation of a transfer function to convert data from a first dataset from a source domain to usable data within a target domain (e.g., domain of a second dataset). Energy function is composed of two terms. First guiding term is a realistic expert, which prioritizes energy function’s focus on discarding source domain-specific features. Second guiding term is a faithful expert, which prioritizes energy function’s focus on preserving domain- independent features. Combination of these two functions results in a target domain data set agnostic of source domain data protocols, but which retains substantive features the data manifested. In combination with the pretrained energy function, EGSDE methods employ three experts (energy function, realistic expert, faithful expert) to all contribute to generating best fit output data. A mathematical explanation of this process is described in detail for time-series data. Let ^^^ ^^^^ be the unknown data distribution on ℝ^. The forward diffusion process ^ ^^௧^௧∈^^,்^, indexed by time t, can then be represented by the following forward SDE: ^^ ^^ ൌ ^^^^^, ^^^^^ ^^ ^ ^^^^^^^^ ^^,where ^^ ∈ ℝ^may be a standard Wiener process, ^^^∙, ^^^: ℝ^→ ℝ^is the drift coefficient and ^^^ ^^^ ∈ ℝ is the diffusion coefficient. As used herein, a “Wiener process” is a filter used in signal processing such that an estimate of a target domain may be produced using linear time- invariant filtering of a noisy process, by assuming known stationary signal. This process may replace the use of a Wiener process with an adaptive filter, least mean squared, frequency domain adaptive filter, or any other applicable filter. As used herein, an “adaptive filter” is a system with a linear filter having a transfer function controlled by variable parameters and a 55 Attorney Docket No.1519-032PCT1 means to adjust those parameters according to an optimization algorithm. The ^^^ ^^, ^^^ and ^^^ ^^^ is related into the noise size and determines the perturbation kernel ^^௧|^^ ^^௧| ^^^^ from time 0 to t. If we allow ^^௧^ ^^^ to be the marginal distribution of the SDE at time t, its time reversal can then be described by a separate SDE: ^^ ^^ ൌ ^ ^^^^^, ^^^െ ^^^ ^^^ଶ∇௬ log ^^௧^ ^^^^ ^^ ^^ ^ ^^^ ^^^ ^^ ^ഥ^ ,where ^ഥ^ is a reverse-time standard Wiener process, and dt is an infinitesimal negative timestep. Then a score-based model, s(y,t), may be adopted to approximate the unknown ∇௬^^ ^^ ^^ ^^௧^ ^^^ by score matching, and thereby inducing a SBDM, which is defined by an SDE: ^^ ^^ ൌ ^ ^^^^^, ^^^െ ^^^ ^^^ଶs^y, t^^ ^^ ^^ ^ ^^^ ^^^ ^^ ^ഥ^ .This may then be discretized using a Euler-Maruyama solver. By formally adopting a step size of h, the iteration rule from s to t = s – h is: ^^௧ ൌ ^^^ െ ^ ^^^^^^, ^^^െ ^^^^^^ଶ^^^^^^, ^^^^ℎ ^ ^^^^^^√ℎ ^^, ^^ ~ Ɲ^0, ^^^.Still of unpaired images from the source domain ^^ ⊂ and the target domain ^^ ⊂ as the training data, the goal is to transfer the original time series data from the source domain to the target domain. This goal is accomplished by designing a distribution ^^^ ^^^| ^^^^ on the target domain ^^ conditioned on a time series ^^^∈ ^^ to transfer. The translated time series data should be realistic for the target domain by changing the domain-specific features and faithful for the source time series by preserving the domain-independent features. An iterative latent variable refinement (ILVR) may then use a diffusion model on the target domain for realism. An ILVR starts from ^^்~ Ɲ^0, ^^^and samples from the diffusion model, described immediately above, for ^^். To promote faithfulness, ^^்may be further refined by adding a residual between the sample ^^்and the perturbed source image ^^்through a non-trainable low-pass filter: ^^்← ^^்^ ɸ^ ^^௧^ െ ɸ^ ^^௧^, ^^௧~ ^^௧|^^ ^^௧| ^^^^, where ɸ^^^is a low- determined by theforward SDE. To most accurately employ the pre-trained energy functions across both domains, a valid conditional distribution ^^^ ^^^| ^^^) is defined by compositing a pretrained SDE and a pretrained energy function under mild regularity conditions as follows: ^^ ^^ ൌ ^ ^^^^^, ^^^െ ^^ ^ ^^^ଶ^ ^^^^^, ^^^െ ∇௬ℰ^^^, ^^^, ^^^^^ ^^ ^^ ^ ^^^^^^^^ ^ഥ^ , 56 Attorney Docket No.1519-032PCT1 where ^ഥ^ is a reverse-time standard Wiener process, dt is an infinitesimal negative timestep such that s^^ | ^^ ∶ ℝ^x ℝ → ℝ^is the score-based model in the pretrained SDE and ℰ^^,^,^^∶ ℝ^x ℝ^x ℝ → ℝ is the energy function. From there, a start point, ^^^, is sampled from the perturbation distribution ^^ெ|^^ ^^ெ| ^^^^, where user may set M = .5T. Transferred time series data is obtained by taking t = 0 following the SDE. Still referring to in reference to the use of EGSDE, the energy function is derived by balancing the need to retain domain-independent features of initial time series data, while appropriately altering the domain-specific features. Based on this balance, the energy function is the sum of two log potential functions: ℰ^^^, ^^, ^^^ൌ ^^^ℰ^^^^, ^^, ^^^^ ^^^ℰ^^^^, ^^, ^^^ൌ ^^^ ^^^^|బ൫ ^^௧ห ^^൯ ^^ௌ^^^, ^^௧ , ^^^െwhere potential functions, ^^௧is the perturbed source image in the forward SDE, ^^௧|^^^ | ^^ is the perturbation kernel from time 0 to time t in the forward SDE, ^^ௌ^^,^,^^ ∶ ℝ^x ℝ^x ℝ → ℝ and ^^^^^,^,^^ ∶ ℝ^x ℝ^x ℝ → ℝ are functions measuring the perturbed time series, and ^^^∈ ℝவ^are weighting hyper-parameters. To specify ^^ௌ^^,^,^^, a time- dependent domain-specific feature extractor ^^ௌ^^,^^ ∶ ℝ^x ℝ → ℝ^^ு^^, where C, H, and W are specific feature descriptors of the output. ^^ௌ^^,^^ is the all but last layer of a classifier trained on both domains to predict whether a time series is from the source domain or the target domain. ^^ௌ^^,^,^^is defined as the cosine similarity between features extracted from generated example and initial time series data as follows: ^^ ^ ^^, ^^∑ாೞ^^^௫^,௧^^ாೞ^^^௬,௧^ௌ ^^௧, ^^ ൌ^,௪ ^^^ ^ ^^^, where ^^^௪^^,^^ ∈ ℝ by a domain-independent feature extractor, ^^ ^,^^ ∶ ℝ^x ℝ → operates as a low- filter preserving domain-independent and discarding domain-specific features. ^^^^^,^,^^ is further defined as the negative squared ^^ଶdistance between features extracted from generated example and initial time series data as follows: ^^^^ ^^, ^^௧, ^^^ ൌ െ|| ^^^^ ^^, ^^^ െ ^^^^ ^^௧, ^^^||ଶଶ. A more pass filter, may be to disentangle learning methods of the separate domains. 57 Attorney Docket No.1519-032PCT1 Still referring to FIG.2, and still in reference to the use of EGSDE, solving the energy- guided reverse-time SDE may be accomplished using the pretrained score-based model s(y,t) and energy function, ℰ(y,x,t), to create generated example from conditional distribution ^^^ ^^^| ^^^^. A fair comparison may be achieved using numerical solvers to approximate trajectories from SDEs. As a non-limiting embodiment, a Euler-Maruyama solver is used herein adopting a step size, h, where the iteration rule from s to t = s - h is: ^^௧ൌ ^^^െ ^ ^^^ ^^, ^^^ െ ^^ ^ ^^^ଶ^ ^^^ ^^^, ^^^ െ ∇௬ℰ^ ^^^, ^^^, ^^^^^ ℎ ^ ^^^ ^^^√ℎ ^^, ^^~ Ɲ^0, ^^^. a non- preserve energy- may be used to modify the noise prediction network and take it into the sampling procedure in denoising diffusion probabilistic models. Still referring to FIG.2, and still in reference to the use of EGSDE, a product of experts is used for the discretized sampling process. A conditional distribution at time, t, is defined: ^^^^ ^^^^ ^ ^^ | ^௧| ^^^^ ൌ^^ ௧ ^^^ ^^^^ ^^௧| ^^^^^^ ,where ^^ is the ^^௧, ^^^, ^^^൯ and ^^^^^ ^^௧| ^^^^ is marginal distribution at time t, based on a pretrained SDE on the target domain. A transition kernel, ^^^^ ^^௧| ^^^^, is constructed based on the desirable equilibrium, ^^^^ ^^௧| ^^^^ ൌ^ ^^^^^^௧|^^^^^^^^^^^|^^^^^^ ^^^, where ^^ ൌ ^^–ℎ:^^^^ ^^ | ^^ ^ ൌ^൫ ^^௧ห ^^^൯^^^௬^|௫బ^௧^ ^^^^௬ ^ ,where ^^^ ^ ^^^^ is the partition ^^^ ^^^,ℎ^,∑^ ^^, ℎ^ ^^^ is transition kernel of the pretrained SDE. Using a Taylor expansion around ^^^ ^^^,ℎ^ to approximate low curvature values of ℰ^^^௧, ^^^, ^^^, the following is obtained: ^^^^^^௧|^^^^^ Ɲ^ ^^^^^^,ℎ^െ ^^^^,ℎ^∇௬ᇲ ℰ^^^ᇱ, ^^^, ^^^|௬ᇲୀఓ^௬బ,^^,^^^^, ℎ^^^^. sampling from a product of experts as described at the beginning of this paragraph. The conditional distribution, initially defined in the beginning of this paragraph, can now be rewritten as: ^^^^ ^^ ^^^^భ൫^^ ൌ^^௧ห ^^^൯^^మ൫ ^^௧ห ^^^൯^^൫ ^^௧ห ^^^൯ 58 Attorney Docket No.1519-032PCT1 where ^^^ଶ^ ^^௧| ^^^^ ∝ exp൫െ ^^^ℰ^^ ^^௧, ^^^, ^^^൯ , ^^^^ ^^௧| ^^^^ ∝ exp൫െ ^^^ℰ^^ ^^௧, ^^^, ^^^൯. With t = 0, faithful expert, corresponding to the score function s(y,t) and the log potential functionsℰ^^^^, ^^, ^^^and ℰ^^^^, ^^, ^^^, respectively.Further referring to FIG.2, one or more processes or algorithms described above may be performed by at least a dedicated hardware unit 232. A “dedicated hardware unit,” for the purposes of this figure, is a hardware component, circuit, or the like, aside from a principal control circuit and / or processor performing method steps as described in this disclosure, that is specifically designated or selected to perform one or more specific tasks and / or processes described in reference to this figure, such as without limitation preconditioning and / or sanitization of training data and / or training a machine-learning algorithm and / or model. A dedicated hardware unit 232 may include, without limitation, a hardware unit that can perform iterative or massed calculations, such as matrix-based calculations to update or tune parameters, weights, coefficients, and / or biases of machine-learning models and / or neural networks, efficiently using pipelining, parallel processing, or the like; such a hardware unit may be optimized for such processes by, for instance, including dedicated circuitry for matrix and / or signal processing operations that includes, e.g., multiple arithmetic and / or logical circuit units such as multipliers and / or adders that can act simultaneously and / or in parallel or the like. Such dedicated hardware units may include, without limitation, graphical processing units (GPUs), dedicated signal processing modules, FPGA or other reconfigurable hardware that has been configured to instantiate parallel processing units for one or more specific tasks, or the like, A computing device, processor, apparatus, or module may be configured to instruct one or more dedicated hardware units to perform one or more operations described herein, such as evaluation of model and / or algorithm outputs, one-time or iterative updates to parameters, coefficients, weights, and / or biases, and / or any other operations such as vector and / or matrix operations as described in this disclosure. Referring now to FIG.3, an exemplary embodiment of neural network 300 is illustrated. A neural network 300 also known as an artificial neural network, is a network of “nodes,” or data structures having one or more inputs, one or more outputs, and a function determining outputs based on inputs. Such nodes may be organized in a network, such as without limitation a 59 Attorney Docket No.1519-032PCT1 convolutional neural network, including an input layer of nodes 304, one or more intermediate layers 308, and an output layer of nodes 312. Connections between nodes may be created via the process of "training" the network, in which elements from a training dataset are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning. Connections may run solely from input nodes toward output nodes in a “feed-forward” network, or may feed outputs of one layer back to inputs of the same or a different layer in a “recurrent network.” As a further non- limiting example, a neural network may include a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. A “convolutional neural network,” as used in this disclosure, is a neural network in which at least one hidden layer is a convolutional layer that convolves inputs to that layer with a subset of inputs known as a “kernel,” along with one or more additional layers such as pooling layers, fully connected layers, and the like. Referring now to FIG.4, an exemplary embodiment of a node 400 of a neural network is illustrated. A node may include, without limitation, a plurality of inputs xithat may receive numerical values from inputs to a neural network containing the node and / or from other nodes. Node may perform one or more activation functions to produce its output given one or more inputs, such as without limitation computing a binary step function comparing an input to a threshold value and outputting either a logic 1 or logic 0 output or something equivalent, a linear activation function whereby an output is directly proportional to the input, and / or a non-linear activation function, wherein the output is not proportional to the input. Non-linear activation functions may include, without limitation, a sigmoid function of the form ^^^^^^ൌ^^ି^ష^given ^ష ^ି^^input x, a tanh (hyperbolic tangent) function, of the form^^ା^ష^, a tanh derivative function such as ^^^ ^^^ ൌ tanhଶ^ ^^^, a rectified linear unit function ^^^ ൌ max ^0, ^^^, a “leaky” and / or “parametric” rectified linear unit function such as ^^^ ^^^ ൌ max ^ ^^ ^^, ^^^ for some a, anexponential linear units function such as ^^^ ^^^ ൌ ^ ^^ ^^ ^^ ^^ ^^ ^ 0^^ ^ 0for some value of ^^(this function may be replaced and / or weighted in some embodiments), a 60 Attorney Docket No.1519-032PCT1 softmax function such as ^^ ^^^ൌ^^ ^ ^∑^ ௫^where the inputs to an instant layer are ^^^, a swish function such as ^^^^^^ൌ ^^ ∗ sigmoid^ ^^^, a Gaussian error linear unit function such as f(x) = ^^൫1 ^ tanh ^^2 / ^^^^^ ^ ^^ ^^^^^൯ for some values of a, b, and r, and / or a scaled exponential linearunit^^^ ^^௫ െ 1^ ^^ ^^ ^^ ^^ ^ 0 ^ ^^ ^^ ^^ ^^ ^^ ^ 0 . Fundamentally, there is no limit to thenature of functions functions. As a non-limiting illustrative may a sum of inputs using weights wi that are multiplied by respective inputs xi. Additionally or alternatively, a bias b may be added to the weighted sum of the inputs such that an offset is added to each unit in the neural network layer that is independent of the input to the layer. The weighted sum may then be input into a function φ, which may generate one or more outputs y. Weight wi applied to an input xi may indicate whether the input is “excitatory,” indicating that it has strong influence on the one or more outputs y, for instance by the corresponding weight having a large numerical value, and / or a “inhibitory,” indicating it has a weak effect influence on the one more inputs y, for instance by the corresponding weight having a small numerical value. The values of weights wi may be determined by training a neural network using training data, which may be performed using any suitable process as described above. Referring now to FIG.5, an exemplary embodiment of a method 500 of real time image generation is illustrated. One or more steps of method 500 may be implemented, without limitation, as described above in reference to FIG.1. One or more steps of method 500 may be implemented, without limitation, using at least a processor. Still referring to FIG.5, in some embodiments, method 500 may include at least an optical system, capturing a first image of a slide at a first position 505. Still referring to FIG.5, in some embodiments, method 500 may include using an output interface, displaying the first image to a user 510. Still referring to FIG.5, in some embodiments, method 500 may include using an input interface, receiving a parameter set from a user 515. Still referring to FIG.5, in some embodiments, method 500 may include using an actuator mechanism, moving an item selected from the list consisting of a slide port, the slide, and the at least an optical system into a second position, wherein the location of the second position is based on the parameter set 520. In some embodiments, moving the item selected from 61 Attorney Docket No.1519-032PCT1 the list consisting of the slide port, the slide, and the at least an optical system into a second position includes using the actuator mechanism, positioning the slide relative to the at least an optical system such that a region of interest defined by the parameter set is within the frame of the at least an optical system; and setting the zoom of the at least an optical system based on a level of zoom specified by the parameter set. In some embodiments, moving the item selected from the list consisting of the slide port, the slide, and the at least an optical system into a second position further includes setting a focus height of the at least an optical system based on a focus height specified by the parameter set. In some embodiments, moving the item selected from the list consisting of the slide port, the slide, and the at least an optical system into a second position further includes detecting a sample height at the region of interest; and setting a focus height of the at least an optical system based on the sample height at the region of interest. Still referring to FIG.5, in some embodiments, method 500 may include using at least an optical system, capturing a second image of the slide at the second position 525. In some embodiments, the second image is captured using a higher level of zoom than that used to capture the first image. Still referring to FIG.5, in some embodiments, method 500 may include using the output interface, displaying the second image to the user 530. In some embodiments, displaying the second image to the user includes replacing a region of the first image with the second image to create a hybrid image; and displaying the hybrid image to the user. Still referring to FIG.5, in some embodiments, method 500 may further include using user interface, receiving an annotation instruction from the user; and adding an annotation to an image selected from the list consisting of the first image, the second image, and the hybrid image based on the annotation instruction. Still referring to FIG.5, in some embodiments, method 500 may further include using and an annotation machine learning model, identifying an image feature; and adding an annotation to an image selected from the list consisting of the first image, the second image, and the hybrid image based on the identification of the image feature. Still referring to FIG.5, in some embodiments, method 500 may further include removing a first artifact from the first image by inputting the first image into an artifact removal machine learning model; and receiving from the artifact removal machine learning model a version of first image lacking the artifact. 62 Attorney Docket No.1519-032PCT1 Still referring to FIG.5, in some embodiments, method 500 may further include removing a second artifact from the second image by: inputting the second image into an artifact removal machine learning model; and receiving from the artifact removal machine learning model a version of second image lacking the artifact. Referring now to FIGS.6–7, exemplary methods are described. In some cases, intraoperative diagnostic assessment of tissue may be of immense value. In some cases, fixing a tissue sample may be performed using one or more of formalin, paraffin, and cryogenic methods. According to some methods, digitizing, modifying, and displaying an image of a slide mounted tissue sample may be performed at rates suitable for intraoperative analysis. For instance, a tissue sample may be biopsied, frozen, sectioned, and mounted on a slide, and then a process may be employed to digitize images of the slide, modify them according to one or methods described in this disclosure and display them to a user, all during an operation. FIG.6 illustrates an exemplary method 600 of modifying an image. In some embodiments, digitization of an image of tissue may result in generation of high-quality images which are immediately received by a processor. Digitization may include any method described in this disclosure, including those using one or more of an optical system and / or an optical sensor. At step 605 method may receive image, for instance using one or more of a computing device and / or processor as described in this disclosure. Image may include a digital image, and receiving image may include receiving a digital signal of image. Receiving image 605 may be performed remote or local to digitization hardware (e.g., optical system and / or optical sensor) or remote to digitization hardware, for instance through cloud transfer and / or network communication. With continued reference to FIG.6, at step 610, method 600 may modify image. In some case, image modification may include one or more of removing artifacts, recognizing annotations, classifying images with annotations. Once modified, image may be displayed to a user. In some cases of method 600, section (e.g., a frozen section of tissue) may be directly viewed by a pathologist with artifacts removed, and with pathological features detected in segments of the digitized slide, thereby enabling interactive viewing and digitizing of the section. Referring now to FIG.7, an exemplary method 700 of real-time image generation is shown. Method 700 may facilitate rapid real-time digitization of a slide with selective high- resolution scans of select regions of the slide with potential pathological features and impurities. 63 Attorney Docket No.1519-032PCT1 In some cases, a tissue section 702 (e.g., frozen tissue section) may be digitized, at step 705. For instance, in some cases, digitization 705 may occur according to a scanning process. Digitization and / or scanning processes may include any digitization or scanning process described in this disclosure, including those incorporated by reference in this disclosure. With continued reference to FIG.7, at step 710, digitized image may be modified to remove impurities. Removal of impurities may be performed according to any image modification / generation process described in this disclosure. In some cases, step 710 may remove artifacts from digital image. In some cases, any machine learning model and / or process described above may be employed for artifact removal. For instance in some cases a diffusion model may be used for image modification, such as removing impurities where the image generation is performed unconditionally or conditionally given detected regions of impurities. With continued reference to FIG.7, step 715 may include modification of digital image conditioned on user input, such as permitting user examination of specific layers at different magnifications, through an interface that emulates traditional microscope controls. In some cases, user input may include audio and / or text input. For instance, text-conditioned (i.e., audio translated to text) examination of select regions of slide may be performed at step 710 through a user interface. User interface may include any user interface described in this disclosure. In some cases, user interface may include one or more of microscope controls, a touch / keyboard, and microphone interface. In some cases, a system (as described throughout this disclosure) coupled with user interface enables user to interactively 725 view modified digital image of slide 720 at different magnification levels as well as high-resolution scans of specific sections. In some cases, at step 725, digitization of new images may follow modification of digitized images. New digitized images may then be modified and combined with earlier images all in real-time, thereby eliminating multiple iterations that could prove costly from a time perspective for intraoperative decision making. Machine learning models as described above may be used to combine images, for instance segmentation models. Additionally, in some cases, interaction with digitized slide image with text input or region demarcation may be performed with text-conditioned diffusion models. In some versions, both from training and inference time perspective diffusion models may be trained on a latent space of quantized vectors where the quantization is performed by a separate Vector Quantized Generative Adversarial Network (VQGAN) model, or the like. 64 Attorney Docket No.1519-032PCT1 In some cases, after image modification 610, image may be displayed using an output device. For instance, image be viewed interactively and verified by a human expert; in some cases, this may occur on the very same device that digitized slide. Referring now to FIG.8, a block diagram of an exemplary apparatus for an exemplary embodiment of an apparatus 100 for generating a confidence score associated with a scanned label is illustrated. In some embodiments, processor 804 may be configured to receive a profile 808 from a user. For the purposes of this disclosure, a “profile” is a representation of information and / or data describing information an individual or a group slides. A profile 808 may be made up of a plurality of slide data. As used in the current disclosure, “data” is information associated with slide. The profile 808 may be a digital representation of a histology slide. As used in the current disclosure, a “histology slide” is a slide containing a portion of biopsied tissue. A histology slide may include biopsied tissue from a patient, wherein the biopsied tissue is sliced into very thin layers and placed on a glass slide. A digital representation of the histology slide may include digital photos of the histology slide. These photos may include digital photos taken under a microscope. A profile 808 may additionally include paperwork surrounding the histology slide. Current disclosure may include information regarding testing, analysis, storage, and disposal of histology slides by medical professionals. A profile 808 may be created by a processor 804, a user, medical professional, or a third party (e.g. Spouse, Support Staff, Family Member, and the like, ). The profile 808 may include any of the following personal information: age, weight, height, gender, geographical location, diagnostic information, medical history, test result, lab result, and the like. A profile 808 may include information medical information associated with patient and / or tissue. With continued reference to FIG 8, a profile 808 contains a label 812 associated with the histology slide. As used in the current disclosure, a “label” is a descriptive tag or identifier that is assigned to an individual or a group histology slides. A label 812 may contain a plurality of information regarding the histology slide. A label 812 may contain information regarding a patient or caser identifier, wherein a unique identifier is assigned to a specific patient or case, which helps in tracking and referencing slides related to that case. A label 812 may contain information regarding the type of biological sample, disease, tissue, morphology, and the like contained within the histology slide. This may include they type of tissue or sample that the slide represents, such as breast tissue, lung biopsy, skin lesion, cardiac tissue, liver tissue, and the like. 65 Attorney Docket No.1519-032PCT1 In some cases, a label 812 may contain information regarding a pathological diagnosis or condition being investigated or identified on the slide, such as carcinoma, lymphoma, or infectious disease. Labels 812 may contain information regarding the specific staining or analysis techniques that have been applied to the histology slide. The specific staining or preparation technique used on the slide, which can provide additional information about the slide's characteristics, including but not limited to techniques such as hematoxylin and eosin (H&E) staining, immunohistochemistry (IHC), or special stains. Labels 812 may include additional notes, observations, or annotations made by the pathologist that may be relevant to the slide, such as the presence of specific features or abnormalities. Labels 812 may additionally include grading labels, staging labels, quality assessment labels, and the like. A grading label may reflect the severity or stage of the disease based on certain criteria. A quality assessment label may reflect the reliability or suitability of the slide for analysis. The specific use of labels may vary depending on the pathology laboratory's protocols and practices. The purpose of labels is to enhance the organization, communication, and retrieval of histology slides within a laboratory or medical facility. With continued reference to FIG 8, a label 812 may come in a plurality formats. The labels 812 may include both printed labels, digital labels, handwritten labels, photographs of labels, scans of labels, and the like. In some cases, a label may take the form of an identification code. As used in the current disclosure, an “identification code” is a visual representation of the label. An identification code may include a barcode. A barcode may consist of a series of parallel lines, bars, or squares of varying widths and spacings. The barcode may serve as a unique identifier for the slide and contains encoded data that provides relevant information about the slide. Identification codes are designed to be scanned or read by barcode scanners or readers, which can quickly decode the encoded information. Identification codes are widely used in various industries for purposes such as product identification, inventory management, and tracking. Identification codes may include both one-dimensional (8D) barcodes and two- dimensional (2D) barcodes. A linear or one-dimensional (8D) barcode, which is composed of a sequence of vertical bars and spaces. The width and spacing of these bars and spaces represent specific patterns that encode alphanumeric or numeric data. Examples of 8D barcodes include the Universal Product Code (UPC) and the Code 39 barcode. A two-dimensional (2D) barcode, which can encode more complex information in a smaller space.2D barcodes use patterns of 66 Attorney Docket No.1519-032PCT1 squares, dots, or other geometric shapes to represent data. Examples of 2D barcodes include the QR code (Quick Response code) and the Data Matrix code. By scanning the barcode associated with the histology slide, healthcare professionals can quickly access the associated information in a digital database or Laboratory Information System (LIS). This facilitates efficient tracking, identification, and retrieval of slides during diagnostic processes, consultations, or research. With continued reference to FIG.8, a profile 808 includes a plurality of metadata 816. As used in the current disclosure, “metadata” refers to descriptive information or attributes that provide context, structure, and meaning to data. Metadata 816 is essentially data about data. Metadata 816 helps in understanding and managing various aspects of data, such as its origin, content, format, quality, and usage. It plays a crucial role in organizing, searching, and interpreting data effectively. Metadata 816 may include descriptive metadata, structural metadata, administrative metadata, technical metadata, provenance metadata, usage metadata, and the like. Metadata 816 may be organized and managed through metadata schemas, standards, or frameworks. These provide guidelines and specifications for capturing, storing, and exchanging metadata in a consistent and structured manner. Common metadata 816 standards include Dublin Core, Metadata Object Description Schema (MODS), and the Federal Geographic Data Committee (FGDC) metadata standard. In some cases, metadata 816 may be associated with a label 812 for a histology slide. Metadata 816 may provide additional descriptive information or attributes that are linked to the slide's label. This metadata 816 provides context and relevant details about the slide, aiding in its identification, categorization, and management within a pathology laboratory or medical facility. The specific metadata 816 associated with a label 812 can vary based on the requirements and practices of the medical facility. Metadata 816 associated with the label may include patient information. Patient information may include data such as the patient's name, unique patient identifier (ID), age, gender, and any other relevant demographic information. Patient information helps in identifying and associating the slide with the correct individual's medical records. Metadata 816 may also include case specific details, wherein case specific details may include information about the specific case or clinical scenario related to the slide. Case specific details may include information about the case number, referring physician, clinical history, relevant symptoms, or any other pertinent details that aid in understanding the context of the slide. In some cases, metadata 816 may include information related to the specific specimen type of the slide. This 67 Attorney Docket No.1519-032PCT1 may include the type of tissue or sample that the slide represents. Metadata 816 may contain notes, comments, or observations made by the pathologist or other medical professional. These annotations might highlight specific features, anomalies, or noteworthy aspects of the slide that are important for interpretation or follow-up analysis. For instance a timestamp reflecting when and where the slide was prepared, analyzed, or labeled can be associated as metadata 816. This information helps in tracking and maintaining a chronological record of slide-related activities. It could be breast tissue, lung biopsy, skin lesion, or any other anatomical or pathological specimen. In some embodiments, metadata may contain information regarding staining or preparation technique, pathological diagnosis, and the like. With continued reference to FIG.8, processor 804 is configured to generate a scanned label 820 as a function of the at least a label 812. As used in the current disclosure, a “scanned label” is a label 812 that has been converted from a physical document or an image to machine encoded text or binary code. A scanned label 820 may refer to the process of capturing or digitizing the information on a label using a scanning device, such as a barcode scanner or an optical character recognition (OCR) system. Scanning the label 812 allows for the automatic extraction and interpretation of the label's content for further processing or integration into a digital system. When a label 812 is scanned, the scanning device captures the visual representation of the label, whether it is a barcode, text, or a combination of both. Processor 804 then then converts the scanned image into a scanned label 820 that can be read and interpreted by a computer or software system. This include converting the image associated with the label 812 into machine encoded text. In a non-limiting example, if the label 812 scanned using a barcode scanner, assuming the label 812 is a barcode. The scanner converts the barcode into a scanned label 820 which includes binary code that represents the encoded data. In another non-limiting example, if the label 812 contains text or alphanumeric characters, an OCR system scans the label and uses image recognition algorithms to identify and convert the characters into a scanned label 820 that includes machine encoded text. The OCR software analyzes the scanned image, identifies the shapes and patterns of the characters, and applies character recognition techniques to convert them into digital text. In some embodiments, once the scanned label 820 is generated textual data may be extracted from the label. Textual data may be associated with the corresponding histology slide in a digital database or Laboratory Information System (LIS). This enables efficient tracking, retrieval, and management of histology slides, as data from the 68 Attorney Docket No.1519-032PCT1 scanned label 820 can be used for various purposes such as patient identification, case management, and slide categorization. With continued reference to FIG 8, processor 804 is configured to generate a scanned label 820 using a text recognition module 824. As used in the current disclosure, a “text recognition module” is a software designed to automatically recognize and extract text from images or scanned document. It is a technology that enables computers to understand and interpret printed or handwritten text characters. The output of a text recognition module 824 may be the extracted text in a machine-readable format, which can be further processed, stored, or analyzed by other applications or systems. The accuracy and performance of a text recognition module 824 may depend on factors such as the quality of the input image, the complexity of the text, the language being recognized, and the robustness of the recognition algorithms. In some embodiments, text recognition modules 824 may find application in various fields, including document digitization, data entry, automated form processing, intelligent character recognition (ICR), and automated reading of printed or handwritten text in areas such as optical mark recognition (OMR), invoice processing, and text-based searching within images or scanned documents. Still referring to FIG.8, a text recognition module 824 may include an optical character recognition (OCR) system. With continued reference to FIG 8, processor may be configured to transform data extracted from a label 812 into a digital format, wherein the digital format may be stored and manipulated electronically by the processor 804. Examples of a digital format may include a textural representation (e.g., plain text, XML, JSON, and the like) or / and a graphical representation (scanned labels that contain graphical elements, such as hand-drawn annotations or symbols, may require this representation, e.g., digital vector graphics or bitmap images). Additionally, or alternatively, digital representation may be further structured using specific data formats, for example, and without limitation, scanned label may be represented using standard formats like DICOM (Digital Imaging and Communications in Medicine) or HL7 (Health Level 7) to ensure interoperability with existing healthcare systems (list some examples here if any), enabling seamless integration with databases and facilitating efficient data exchange and interoperability. 69 Attorney Docket No.1519-032PCT1 With continued reference to FIG.8, processor 804 may be configured to generate a plurality of named entities 828 as a function of the scanned label 820 using a named entity recognition process. As used in the current disclosure, a “named entity” is a specific type of word or phrase that represents a real-world object with a unique identity. Named entities are typically people, places, ideas, concepts, or things that denote specific individuals, organizations, locations, dates, times, products, events, quantities, diseases, tissue samples, and other entities that can be uniquely identified. These entities play a significant role in understanding the context and extracting meaningful information from text. Named entities may provide contextual information and serve as reference points for understanding the meaning and relationships within a text. Recognizing and extracting named entities from textual data is a fundamental task in natural language processing (NLP), information extraction, text mining, and various other applications where understanding the semantics and identifying key elements of text is important. With continued reference to FIG 8, processor 804 may be configured to generate a plurality of named entities 828 using a named entity recognition (NER) system 832. As used in the current disclosure, a “named entity recognition (NER) system” is software that identifies a plurality of named entities 828 in from text. A NER system 832 may be configured to identify a plurality of named entities from a scanned label 820. Inputs of a NER system 832 may include a profile 808, label 812, scanned label 820, metadata, and the like. The output of a named entity recognition system 832 may include a plurality of named entities 828. Named entities 828 may include a structured representation of the identified named entities, typically in the form of annotations or tags attached to the original text. With continued reference to FIG 8, a NER system 832 may generate a plurality of named entities 828 using a natural language processing model. As used in the current disclosure, a “natural language processing (NLP) model” is a computational model designed to process and understand human language. It leverages techniques from machine learning, linguistics, and computer science to enable computers to comprehend, interpret, and generate natural language text. The NLP model may preprocess the input text, wherein the input text may include the label 812 and the scanned label 820, or any other data mentioned herein. Preprocessing the input text may involve tasks like tokenization (splitting text into individual words or sub-word units), normalizing the text (lowercasing, removing punctuation, etc.), and encoding the text into a 70 Attorney Docket No.1519-032PCT1 numerical representation suitable for the model. The NLP model may include transformer architecture, wherein the transformers are deep learning models that employ attention mechanisms to capture the relationships between words or sub-word units in a text sequence. They consist of multiple layers of self-attention and feed-forward neural networks. The NLP model may weigh the importance of different words or sub-word units within a text sequence while considering the context. It enables the model to capture dependencies and relationships between words, taking into account both local and global contexts. This process may be used to identify a plurality of named entities 828. Language processing model may include a program automatically generated by processor 804 and / or named entity recognition system to produce associations between one or more significant terms extracted from a scanned label 820 and detect associations, including without limitation mathematical associations, between such significant terms. Associations between language elements, where language elements include for purposes herein extracted significant terms, relationships of such categories to other such term may include, without limitation, mathematical associations, including without limitation statistical correlations between any language element and any other language element and / or language elements. Statistical correlations and / or mathematical associations may include probabilistic formulas or relationships indicating, for instance, a likelihood that a given extracted significant term indicates a given category of semantic meaning. As a further example, statistical correlations and / or mathematical associations may include probabilistic formulas or relationships indicating a positive and / or negative association between at least an extracted significant term and / or a given semantic relationship; positive or negative indication may include an indication that a given document is or is not indicating a category semantic relationship. Whether a phrase, sentence, word, or other textual element in scanned label 820 constitutes a positive or negative indicator may be determined, in an embodiment, by mathematical associations between detected significant terms, comparisons to phrases and / or words indicating positive and / or negative indicators that are stored in memory at processor 804, or the like. With continued reference to FIG.8, processor 804 may classify a plurality of named entities 828 into a plurality of entity categories. As used in the current disclosure, “entity categories” is a category that is representative of one or more predefined classes or types of data. Entity categories may be related to one or more aspects of the histology slide. Entity categories may include broad areas or aspects of a histology slide. Non-limiting examples of entity 71 Attorney Docket No.1519-032PCT1 categories may include patient name, patient identification code, slide identification code, specimen identification code, specimen type, stains or preparation techniques, alphanumeric identification codes, pathological diagnosis, scoring or ranking of the histology slide, annotations, observations, notes, medical facility name, medical facility identification number, and the like. In an embodiment, a processor 804 may be configured to generate a plurality of entity categories based on the available the scanned label 820. Processor 804 may generate a plurality of entity categories based on historical versions of the entity categories. Processor 804 may generate a plurality of entity categories by extracting relevant features, characteristics, or traits associated with the scanned label 820. Identification of features may depend on the nature and type of histology slide. In a non-limiting example, plurality of named entities 828 may be classified into a plurality of entity categories based on their semantic meaning. In other embodiments, a processor 804 be configured to receive a plurality of entity categories from a database such as database. In an embodiment, entity categories may be used to retrieve regular expression associated with each named entity from the database. With continued reference to FIG.8, processor 804 may be configured to classify each named entity into the plurality of entity categories based on the named entities 828 spatial position on the scanned label 820. As used in the current disclosure, “spatial position” refers to the positioning of the named entity on the label 812. Spatial positioning may be a form of template base NER. Template-based Named Entity Recognition (NER) is an approach to identifying named entities in text using predefined templates or patterns. It relies on a set of fixed patterns that represent the structure or characteristics of the named entities you want to extract. In the current case, the template may include a plurality of fields in fixed spatial positions so that processor 804 is able assign the content of each of those fields to an entity category. The spatial position on the label 812 may be associated with one or more entity categories. The fields on a label 812 may contain important information related to the slide and its associated data. These fields may provide identification, categorization, and contextual details about the slide. The specific fields on a label 812 may be configured to have the same spatial positioning. In a non- limiting example, a label 812 may include a spatial position or a field for a person’s name. To capture person names, processor 804 may consult the lookup table, mentioned herein below, to retrieve a list of know person names, populating the person’s name in the spatial position for a person’s name. can vary depending on the laboratory or institution. Each field on a label 812 72 Attorney Docket No.1519-032PCT1 may be associated with an entity category. Thus, processor 804 may be configured to classify each named entity 828 into entity categories based on the spatial position on a label 812. Processor 804 may be configured to identify spatial positions on the scanned label as a function of the medical facility that the scanned label 820 is associated with. Processor 804 the medical facility may be identified using an alphanumeric code or another identifier on the label 812 or within its associated metadata 816. With continued reference to FIG 8, a processor 804 may determine a plurality of named entities using a lookup table. A “lookup table,” for the purposes of this disclosure, is a data structure, such as without limitation an array of data, that maps input values to output values. A lookup table may be used to replace a runtime computation with an indexing operation or the like, such as an array indexing operation. A look-up table may be configured to pre-calculate and store data in static program storage, calculated as part of a program's initialization phase or even stored in hardware in application-specific platforms. Data within the lookup table may include previous examples of named entities compared to the scanned label 820. Data within the lookup table may be received from database. Lookup tables may also be used to generate a plurality of named entities 828 by matching an input value to an output value by matching the input against a list of valid (or invalid) items in an array. In a non-limiting example, a scanned label 820 include a plurality of fields containing text that describes various aspects of the slide. Examples of named entities may indicate that a list of named entities from this particular medical facility includes a patient name, patient identification number, sample identification number, and a listing of the staining or preparation techniques used on the slide. A lookup table may look up the scanned label 820 as an input and output a list of named entities 828. Processor 804 may be configured to “lookup” or input one or more scanned label 820, label 812, profile 808, metadata 816, examples of named labels, and the like. Whereas the output of the lookup table may include a list of named entities. Alternatively or additionally, a query representing elements of scanned label 820 may be submitted to the lookup table and / or a database, and an associated data fault identifier stored in a data record within the lookup table and / or database may be retrieved using the query. With continued reference to FIG.8, processor 804 may be configured to generate a scanned label 820 using a label machine-learning model. As used in the current disclosure, a “label machine-learning model” is a machine-learning model that is configured to generate a 73 Attorney Docket No.1519-032PCT1 scanned label. The label machine-learning model may be consistent with the machine-learning model or a classifier. Inputs to the label machine-learning model may include profile 808, label 812, metadata 816, a plurality of entity categories, examples of scanned labels 820, examples of named entities 828, and the like. Outputs to the label machine-learning model may include a scanned label 820 and a listing of named entities. In some embodiments, a label machine learning model may be configured to sort a listing of named entities 828 into one or more entity categories. Label training data is a plurality of data entries containing a plurality of inputs that are correlated to a plurality of outputs for training a processor by a machine-learning process. In an embodiment, label training data may comprise a plurality of labels 812 correlated to examples of scanned labels 820. In another embodiment, label training data may comprise a plurality of labels 812 correlated to examples of named entities. Label training data may be received from database. Label training data may contain information regarding profile 808, label 812, metadata 816, a plurality of entity categories, examples of scanned labels 820, examples of named entities 828, and the like. Machine-learning models may be performed using, without limitation, linear machine-learning models such as without limitation logistic regression and / or naive Bayes machine-learning models, nearest neighbor machine-learning models such as k-nearest neighbors machine-learning models, support vector machines, least squares support vector machines, fisher’s linear discriminant, quadratic machine-learning models, decision trees, boosted trees, random forest machine-learning models, learning vector quantization, and / or neural network- based machine-learning models. With continued reference to FIG 8, processor 804 determines a confidence score 836 as a function of a comparison between the scanned label 820 and a plurality of previously scanned labels 820. As used in the current disclosure, a “confidence score” is quantitative measurement of the accuracy of the content generated from scanned label 820. Accuracy of the content of the scanned label 820 may refer to an accurate translation of the text of the label 812 when generating the scanned label 820. Accuracy of the content of the scanned label 820 may also refer to the likelihood that text of the scanned label 820 accurately reflects the content of the histology slide. A processor 804 may generate A confidence score 836 for each attribute of each entity. A confidence score 836 may be used to normalize one or more scanned labels 820 to bring all scanned labels 820 onto a comparable scale. This step is important to eliminate any bias introduced by different qualities or views of the scanned labels. Normalization techniques can 74 Attorney Docket No.1519-032PCT1 include min-max scaling, z-score normalization, or logarithmic transformation. In an embodiment, if content that is generated from the scanned label 820 is accurate then the confidence score 836 may be high, conversely if content that is generated from the scanned label 820 is likely inaccurate then the confidence score 836 may be low. A confidence score 836 may be expressed as a numerical score, a linguistic value, alphanumeric score, or an alphabetical score. Confidence score 836 may be represented as a score used to reflect the level of accuracy of the content of the scanned label 820. A non-limiting example, of a numerical score, may include a scale from 8-80, 8-800, 8-8000, and the like, wherein a rating of 8 may represent an inaccurate scanned label 820, whereas a rating of 80 may represent an accurate scanned label 820. In another non-limiting example, linguistic values may include, “Highly Accurate,” “Moderately Accurate,” “Moderately Inaccurate,” “Highly Inaccurate,” and the like. In some embodiments, linguistic values may correspond to a numerical score range. For example, a scanned label 820 that receives a score between 50-75, on a scale from 8-800, may be considered “Moderately Accurate.” With continued reference to FIG.8, a confidence score 836 may be generated by comparing the current scanned label 820 to historically scanned labels. As used in the current disclosure, “historically scanned labels” are scanned labels 820 that have been generated prior to the current iteration of scanned labels 820. Processor 804 may identify a plurality of historically scanned labels as a function of the metadata 816 associated with the label 812. The metadata 816 may contain information regarding patient identifiers or medical facility identifiers. Based on the patient identifiers and / or medical facility identifiers processor 804 may generate a plurality of historically scanned labels from the same facility and / or patient of the current scanned label 820. Processor 804 may compare the historically scanned labels the current scanned label 820 may be comparing their content, spatial position, font, and the like. To compare a scanned label 820 to historically scanned labels to verify the accuracy of the scanned label 820, a processor 804 may implement a process that involves data matching and comparison. Processor 804 may extract relevant features or attributes are extracted from the current scanned label 820 and the historically scanned labels. These features could include specific fields or data elements such as slide IDs, patient IDs, specimen types, dates of collection, or any other relevant information present on the label. Processor 804 may then apply a comparison algorithm to evaluate the similarity or differences between the extracted features of the current scanned label 820 and the 75 Attorney Docket No.1519-032PCT1 historically scanned labels. The choice of the comparison algorithm depends on the specific requirements and nature of the data. For example, it could be a simple string comparison, machine learning model, fuzzy matching algorithm, or more advanced techniques like Levenshtein distance or token-based similarity measures. Processor 804 may determine if there is a match or similarity threshold that indicates the accuracy of the current scanned label. If a match is found, it can be considered as a verification of the accuracy of the label. If there are discrepancies or differences, it may require further investigation or manual verification. Processor 804 may assign a confidence score 836 to indicate the level of accuracy or similarity between the current scanned label 820 and the historically scanned labels. This score can be based on the results of the comparison algorithm and can be used to make decisions or trigger appropriate actions based on predefined thresholds. In an embodiment a historically scanned label may be a barcode as mentioned herein above. With continued reference to FIG.8, a confidence score 836 may include a derivation score. As used in the current disclosure, a “derivation score” is a score that addresses variations in scanned label 820 as compared to historical scanned labels. These variations may be caused due to errors in the OCR system performance due to text attributes like font, boldness, italics, handwriting, etc. If the OCR system does not provide the same result for several OCR scans associated with the same user, the derivation score may be affected negatively. Processor 804 may be configured to preprocess the scanned label 820. Preprocessing may include steps to normalize the text attributes within the scanned label 820. This could involve standardizing the font, removing unnecessary formatting, or converting the text to a consistent style (e.g., removing italics or boldness). Processor 804 may then extract relevant features or attributes are extracted from the scanned label and the previously scanned labels. These features could include the text content, font type, font size, boldness, italics, or any other relevant attributes that may impact OCR performance. Processor 804 may then compare the OCR results of the scanned label 820 with the OCR results of the historically scanned labels. It assesses the variations or differences in the OCR output, taking into account text attributes like font, boldness, italics, or handwriting styles. Based on the comparison results, a derivation score may be calculated to quantify the level of variation or inconsistency in the OCR performance. This score takes into account the frequency and magnitude of variations in the OCR output associated with different text attributes. The derivation score may serve as an indicator of the OCR system's performance 76 Attorney Docket No.1519-032PCT1 consistency. A lower derivation score suggests a lower likelihood of variations in OCR results due to text attributes, which may decrease confidence in the accuracy of the detection. Conversely, a higher derivation score indicates more consistent OCR performance across different scans of the same user, increasing confidence in the detection. With continued reference to FIG.8, a confidence score 836 may include an expression score. As used in the current disclosure, an “expression score” is a score that represents OCR system’s translation of one or more words within scanned label. An expression score may be reflected for each word in the scanned label 820 or it may be aggregated to provide one score for all of the words included in the scanned label 820. Processor 804 may be configured to preprocess the scanned label 820. Preprocessing may include steps to normalize the text attributes within the scanned label 820. This could involve standardizing the font, removing unnecessary formatting, or converting the text to a consistent style (e.g., removing italics or boldness). Processor 804 may then extract a plurality of words from the scanned label 820. In some embodiments, an expression score may include a classification of each word of the scanned label 820 to a named entity 828. This classification can be performed by mapping the expression scores to a predefined set of named entities or categories. For each extracted word, processor 804 calculates an expression score that reflects the match or alignment with the expected named entity category. This score takes into account the presence or absence of the named entity's associated with the extracted word. The expression scores may serve as a confidence metric for the accuracy of the named entity classification. If the text extracted by OCR does not match a named entity 828 of the plurality of named entities 828, then the confidence goes down. A higher expression score indicates a better alignment with the expected named entity, while a lower score suggests a potential error or mismatch. With continued reference to FIG.8, a confidence score 836 may include a consistency score. As used in the current disclosure, a “consistency score” is a score that represents confidence in a barcode. As mentioned herein above, a scanned label 820 may include one or more barcodes. The scanned label 820 (barcode) obtained through the OCR process may undergo preprocessing steps to normalize and clean the data. This could involve removing noise, correcting errors, or formatting the text. Processor 804 applies barcode recognition techniques to extract the barcode information from the scanned label 820. This process can involve decoding the barcode using specific algorithms or libraries designed for barcode recognition. Processor 77 Attorney Docket No.1519-032PCT1 804 then extracts any accompanying text or information present along with the barcode using the text recognition system, mentioned herein above. Processor 804 may compare the content described by the extracted barcode with the historically scanned label or the OCR scan. It evaluates if there is a difference or mismatch between the information derived from the barcode and historically scanned labels. Based on the content comparison, the processor 804 may generate a consistency score that reflects the level of consistency between the barcode and the historically scanned label or other OCR generated text. If there is a difference in the content described by the barcode and the OCR scan, the confidence goes down, resulting in a lower consistency score. The generated Consistency score provides a measure of the accuracy between the scanned label 820 (barcode) and historically scanned label or / and the text obtained through OCR recognition. It helps assess the reliability and confidence in the accuracy of the scanned label. By evaluating the consistency score, the computer can make informed decisions, trigger appropriate actions, or initiate further verification steps based on predefined confidence thresholds. With continued reference to FIG.8, a confidence score 836 may include a temporal consistency score. As used in the current disclosure, a “temporal consistency score” is a score that addresses errors incurred due to limitations inconsistencies in handwriting or printed text. A temporal consistency score may be generated by comparing the current OCR scan to other documents associated with the same user to catch the degradation and help lower confidence. For example, if a handwritten document contains the letter “R” but the OCR system interprets the letter as “K” due to unclear handwriting. Processor 804 may compare the handwritten document to other documents in the set to identify a discrepancy between the scanned label 820 and historically scanned labels. The temporal consistency score may reflect the confidence processor 804 has in the scanned label. In the above mentioned example, the temporal consistency score may be negatively affected due to the misinterpretation of the letter “R.” Processor 804 may place the scanned label 820 through preprocessing steps to normalize and clean the data. This includes removing noise, correcting errors, and formatting the text for comparison. Processor 804 then compares the current scanned label 820 to historically scanned labels. This comparison aims to identify any inconsistencies or discrepancies in the text due to limitations in handwriting or printed text quality. Based on the text comparison, the processor 804 may calculate a temporal consistency score that reflects the level of consistency or inconsistency between the current 78 Attorney Docket No.1519-032PCT1 scanned label 820 and the historically scanned labels. This score helps assess the temporal consistency and potential degradation of text recognition accuracy. With continued reference to FIG 8, Processor 804 may generate a confidence score 836 as a function of the temporal consistency score, consistency score, expression score, and a derivation score. This embodiment of a confidence score 836 may be an overall reflection of the confidence the content generated from scanned label 820. In an embodiment, a confidence score 836 may be generated by averaging two or more of the above mentioned scores. This may include assigning each score a weighted average. In another embodiment, a confidence score 836 may be generated by evaluating the highest or lowest score among the four as the confidence score 836. With continued reference to FIG.8, processor 804 may generate the confidence score 836 using a score machine-learning model 840. As used in the current disclosure, a “score machine- learning model” is a machine-learning model that is configured to generate a confidence score 836. The score machine-learning model 840 may be consistent with the machine-learning model. Inputs to the score machine-learning model 840 may include profile 808, label 812, metadata 816, a plurality of entity categories, scanned labels 820, named entities 828, examples of confidence scores 836, and the like. Outputs to the score machine-learning model 840 may include a confidence score 836 tailored to one or more scanned labels 820. Outputs to the score machine learning model may additionally include a variation score, expression score, consistency score, and temporal consistency score. Score training data may include a plurality of data entries containing a plurality of inputs that are correlated to a plurality of outputs for training a processor by a machine-learning process. In an embodiment, score training data may include a plurality of scanned labels 820 correlated to examples of confidence scores 836. Examples of confidence scores 836 may include historical confidence scores 836 that have been generated from previous iterations of score machine learning model or apparatus 800. Score training data may be received from database. Score training data may contain information regarding profile 808, label 812, metadata 816, a plurality of entity categories, scanned labels 820, named entities 828, examples of confidence scores 836, and the like. In an embodiment, a score machine-learning model 840 may be iteratively updated with the input and output results of past score machine-learning models. The machine-learning model may be performed using, without limitation, linear machine-learning models such as without limitation logistic regression 79 Attorney Docket No.1519-032PCT1 and / or naive Bayes machine-learning models, nearest neighbor machine-learning models such as k-nearest neighbors machine-learning models, support vector machines, least squares support vector machines, fisher’s linear discriminant, quadratic machine-learning models, decision trees, boosted trees, random forest machine-learning model, and the like. With continued reference to FIG.8, processor 804 may generate the confidence score 836 as a function of a comparison between the scanned label 820 and the historically scanned labels using a comparison fuzzy inference. As used in the current disclosure, a “comparison fuzzy inference” is a method that interprets the values in the input vector (i.e., scanned label 820 and historically scanned labels.) and, based on a set of rules, assigns values to the output vector. A set of fuzzy rules may include a collection of linguistic variables that describe how the system should make a decision regarding classifying an input or controlling an output. Fuzzy inference rules operate on fuzzy sets and provide a framework for mapping input variables to output variables through linguistic rules. Fuzzy inference rules may operate using linguistic variables, which represent imprecise or vague concepts rather than precise numerical values. Linguistic variables are defined by membership functions, which describe the degree of membership or truth for different linguistic terms or categories. In a non-limiting example, a linguistic variable associated with the confidence score 836 may have linguistic terms like "High Confidence," "Moderate Confidence," and / or “Low Confidence," each with its corresponding membership function. A fuzzy inference rule typically follows a conditional "IF-THEN" structure. It consists of an antecedent (IF part) and a consequent (THEN part). The antecedent specifies the conditions or criteria based on which the rule will be applied, and the consequent determines the output or conclusion of the rule. In an embodiment. the confidence score 836 may be determined by a comparison of the degree of match between a first fuzzy set and a second fuzzy set, and / or single values therein with each other or with either set, which is sufficient for purposes of the matching process. Still referring to FIG.8, confidence score 836 may be determined as a function of the intersection between two fuzzy sets, wherein each fuzzy set may be representative of a scanned label 820 and a historically scanned labels respectively. Comparing the scanned label 820 and a historically scanned labels may include utilizing a fuzzy set inference system as described herein below, or any scoring methods as described throughout this disclosure. For example, without limitation, processor 804 may use a fuzzy logic model to determine confidence score 836 as a 80 Attorney Docket No.1519-032PCT1 function of fuzzy set comparison techniques as described in this disclosure. In some embodiments, each piece of information associated with a scanned label 820 may be compared to a historically scanned labels, wherein the confidence score 836 may be represented using a linguistic variable on a range of potential numerical values, where values for the linguistic variable may be represented as fuzzy sets on that range; a “good” or “ideal” fuzzy set may correspond to a range of values that can be characterized as ideal, while other fuzzy sets may correspond to ranges that can be characterized as mediocre, bad, or other less-than-ideal ranges and / or values. In embodiments, these variables may be used to compare a scanned label 820 and a historically scanned labels to determine the confidence score 836 specific to the scanned label 820. A fuzzy inferencing system may combine such linguistic variable values according to one or more fuzzy inferencing rules, including any type of fuzzy inferencing system and / or rules as described in this disclosure, to determine a degree of membership in one or more output linguistic variables having values representing ideal overall performance, mediocre or middling overall performance, and / or low or poor overall performance; such mappings may, in turn, be “defuzzified” as described in further detail below to provide an overall output and / or assessment. Still referring to FIG.8, processor 804 may be configured to display confidence score 836 using a display device 844. As used in the current disclosure, a “display device” is a device that is used to display content. A display device 844 may include a user interface. A “user interface,” as used herein, is a means by which a user and a computer system interact; for example, through the use of input devices and software. A user interface may include a graphical user interface (GUI), command line interface (CLI), menu-driven user interface, touch user interface, voice user interface (VUI), form-based user interface, any combination thereof, and the like. A user interface may include a smartphone, smart tablet, desktop, or laptop operated by the user. In an embodiment, the user interface may include a graphical user interface. A “graphical user interface (GUI),” as used herein, is a graphical form of user interface that allows users to interact with electronic devices. In some embodiments, GUI may include icons, menus, other visual indicators, or representations (graphics), audio indicators such as primary notation, and display information and related user controls. A menu may contain a list of choices and may allow users to select one from them. A menu bar may be displayed horizontally across the screen such as pull down menu. When any option is clicked in this menu, then the pull down menu may appear. A menu may include a context menu that appears only when the user performs a specific 81 Attorney Docket No.1519-032PCT1 action. An example of this is pressing the right mouse button. When this is done, a menu may appear under the cursor. Files, programs, web pages and the like may be represented using a small picture in a graphical user interface. For example, links to decentralized platforms as described in this disclosure may be incorporated using icons. Using an icon may be a fast way to open documents, run programs etc. because clicking on them yields instant access. Information contained in user interface may be directly influenced using graphical control elements such as widgets. A “widget,” as used herein, is a user control element that allows a user to control and change the appearance of elements in the user interface. In this context a widget may refer to a generic GUI element such as a check box, button, or scroll bar to an instance of that element, or to a customized collection of such elements used for a specific function or application (such as a dialog box for users to customize their computer screen appearances). User interface controls may include software components that a user interacts with through direct manipulation to read or edit information displayed through user interface. Widgets may be used to display lists of related items, navigate the system using links, tabs, and manipulate data using check boxes, radio boxes, and the like. Referring now to FIG.9A, an exemplary embodiment of an imaging device 900 for image generation for specimen with variable thickness is illustrated. In some embodiments, imaging device 900 may include one or more sensors for capturing image signals representative of an image of a scene (e.g., a scene including specimen 912). For instance, and without limitation, a sensor may include a light sensor, image sensor (as described further below), focal plane array, and the like. In various embodiments, sensors may provide for representing and / or converting a captured image signal of a scene to digital data. For instance, and without limitation, sensor may include an analog-to-digital converter. In one or more embodiments, processor 904 may be adapted to receive image signals from imaging device 900 (e.g., image sensor), process image signals to provide processed image data, store image signals and / or image data in memory 908, and / or retrieve stored image signals and / or image data from memory 908 (e.g., for compilation or combinations as discussed further in this disclosure). In one or more embodiments, processor 904 may be configured to process image signals stored in memory 908 to provide image data to display for viewing by a user and / or operator. Still referring to FIG.9A, in one or more embodiments, imaging device may include and / or be communicatively connected to a display, which is discussed further in FIG.8. In one 82 Attorney Docket No.1519-032PCT1 or more embodiments, display may be configured to display image data and any other information described in this disclosure, such as annotations or text. In one or more embodiments, processor 904 may be configured to retrieve image data and information from memory 908 and display such image data and information on display. In other embodiments, display may receive image data directly from optical system, such as optical system 920 (e.g., optical sensor). Still referring to FIG.9A, imaging device 900 may include a user input and / or user interface. For instance, and without limitation, user interface may include one or more user actuated components, such as, for example, one or more push buttons, joysticks, slide bars, rotatable knobs, a mouse, a keyboard, touchscreens, and the like that may be configured to generate one or more input control signals, where an input control signal may include a signal to capture an image from a scene, combine images and / or image data, compile images and / or image data, alter modes of operation of imaging device, alter zoom and / or levels of zoom, alter focus, and the like. User input signals may be generated using user interface and transmitted to processor 904, memory 908, display, optical system 920, and / or any other component of and / or communicatively connected to imaging device 900. In one or more embodiments, processor may be configured to alter or set modes of operation of imaging device, such as but not limited to, auto-focus, contrast, gain (e.g., variable gain), field of view (FOV), brightness, offset, menu enablement and selection, spatial setting, temporal setting, and the like. With continued reference to FIG.9A, in some embodiments, imaging device 900 may be used to generate an image of a specimen 912. For the purposes of this disclosure, a “specimen” is a sample of organic material used for testing or observation purposes. In one or more embodiments, specimen may include a pathology sample. For instance, and without limitation, a specimen may include a sample of interest, including tissue, plasma, or fluid from an individual. For example, and without limitation, specimen 912 may include tissue from an organ, such as a kidney, of an individual (e.g., patient). In some embodiments, specimen 912 may include a tissue sample. In some embodiments, specimen 912 may be frozen. In some embodiments, specimen 912 may be fresh or recently harvested. In one or more embodiments, specimen 912 may include a variable thickness. For instance, and without limitation, specimen 912 may have a different thickness or depth at various locations along specimen 912. For example, and without limitation, 83 Attorney Docket No.1519-032PCT1 specimen 912 may have a first thickness t at a first location x, a second thickness t’ at a second location x’, and a third thickness t” at a third location x”. With continued reference to FIG.9A, in one or more embodiments, specimen 912 may be disposed on a slide 916. As used in this disclosure, a “slide” is a container or surface for holding a specimen. In some embodiments, slide 916 may include a formalin fixed paraffin embedded slide. In some embodiments, specimen 912 on slide 916 may be stained. In some embodiments, slide 916 may be substantially transparent. In some embodiments, slide 916 may include a glass slide. In some embodiments, slide 916 may include a thin, flat, and substantially transparent glass slide. In some embodiments, a cover, such as a transparent cover, may be applied to slide 916 such that specimen 912 is disposed between slide 916 and cover. For example, and without limitation, specimen 912 may be compressed between slide 916 and corresponding cover. Still referring to FIG.9A, in some embodiments, slide 916 and / or a sample on slide 916 may be illuminated. In some embodiments, imaging device 900 may include a light source. As used in this disclosure, a “light source” is any device configured to emit electromagnetic radiation. In some embodiments, light source may emit a light having substantially one wavelength. In some embodiments, light source may emit a light having a wavelength range. Light source may emit, without limitation, ultraviolet light, visible light, and / or infrared light. In non-limiting examples, light source may include a light-emitting diode (LED), an organic LED (OLED) and / or any other light emitter. Such a light source may be configured to illuminate slide 916 and / or specimen 912 on slide 916. In a non-limiting example, light source may illuminate slide 916 and / or specimen 912 on slide 916 from below. In a non-limiting example, light source may illuminate slide 916 and / or specimen 912 on slide 916 from above. Still referring to FIG.9A, in some embodiments, imaging device 900 may include at least an optical system 920. As used in this disclosure, an "optical system" is an arrangement of one or more components which together act upon or employ electromagnetic radiation, such as light (e.g., visible light, infrared light, UV light, or the like). Optical system 920 may include one or more optical elements, including without limitation lenses, mirrors, windows, filters, and the like. Optical system 920 may form an optical image that corresponds to an optical object. For instance, and without limitation, optical system 920 may form an optical image at or upon an optical sensor, which can capture, e.g., digitize, the optical image. In some cases, optical system 920 may have at least a magnification. For instance, and without limitation, optical system 920 84 Attorney Docket No.1519-032PCT1 may include an objective (e.g., microscope objective) and one or more reimaging optical elements that together produce an optical magnification. In some cases, a degree of optical magnification may be referred to herein as zoom. As used herein, an “optical sensor” is a device that measures light and converts the measured light into one or more signals; one or more signals may include, without limitation, one or more electrical signals. In some embodiments, optical sensor may include at least a photodetector. As used herein, a “photodetector” is a device that is sensitive to light and thereby able to detect light. In some embodiments, a photodetector may include a photodiode, a photoresistor, a photosensor, a photovoltaic chip, and the like. In some embodiments, optical sensor may include a plurality of photodetectors. Optical sensor may include, without limitation, a camera. Optical sensor may be in electronic communication with at least a processor 904 of imaging device 900. As used herein, “electronic communication” as used in this disclosure is a shared data connection between two or more devices. In some embodiments, imaging device 900 may include two or more optical sensors. Still referring to FIG.9A, in some embodiments, optical system 920 may include a camera. In some cases, a camera may include one or more optics. Exemplary non-limiting optics include spherical lenses, aspherical lenses, reflectors, polarizers, filters, windows, aperture stops, and the like. In some embodiments, one or more optics associated with a camera may be adjusted in order to, in non-limiting examples, change the zoom, depth of field, and / or focus distance of the camera. In some embodiments, one or more of such settings may be configured to detect a feature of a sample on slide 916. In some embodiments, one or more of such settings may be configured based on a parameter set, as described below. In some embodiments, camera may capture images at a low depth of field. In a non-limiting example, camera may capture images such that a first depth of sample is in focus and a second depth of sample is out of focus. In some embodiments, an autofocus mechanism may be used to determine focus distance. In some embodiments, focus distance may be set by parameter set. In some embodiments, camera may be configured to capture a plurality of images at different focus distances. In a non-limiting example, camera may capture a plurality of images at different focus distances, such that images are captured where each focus depth of the sample is in focus in at least one image. In some embodiments, at least a camera may include an image sensor. Exemplary non-limiting image sensors include digital image sensors, such as without limitation charge-coupled device (CCD) sensors and complimentary metal-oxide-semiconductor (CMOS) sensors. In some embodiments, 85 Attorney Docket No.1519-032PCT1 a camera may be sensitive within a non-visible range of electromagnetic radiation, such as without limitation infrared. Still referring to FIG.9A, in some embodiments, imaging device 900 may include a machine vision system. Machine vision system may include optical system 920 or may be communicatively connected to optical system 920, processor 904, memory 908, and the like. In some embodiments, a machine vision system may include at least a camera. A machine vision system may use images, such as images from at least a camera, to make a determination about a scene, space, and / or object. For example, in some cases a machine vision system may be used for world modeling or registration of objects within a space. In some cases, registration may include image processing, such as without limitation object recognition, feature detection, edge / corner detection, and the like. Non-limiting example of feature detection may include scale invariant feature transform (SIFT), Canny edge detection, Shi Tomasi corner detection, and the like. In some cases, registration may include one or more transformations to orient a camera frame (or an image or video stream) relative a three-dimensional coordinate system; exemplary transformations include without limitation homography transforms and affine transforms. In an embodiment, registration of first frame to a coordinate system may be verified and / or corrected using object identification and / or computer vision, as described above. For instance, and without limitation, an initial registration to two dimensions, represented for instance as registration to the x and y coordinates, may be performed using a two-dimensional projection of points in three dimensions onto a first frame, however. A third dimension of registration, representing depth and / or a z-axis, may be detected by comparison of two frames; for instance, where first frame includes a pair of frames captured using a pair of cameras (e.g., stereoscopic camera also referred to in this disclosure as stereo-camera), image recognition and / or edge detection software may be used to detect a pair of stereoscopic views of images of an object; two stereoscopic views may be compared to derive z-axis values of points on object permitting, for instance, derivation of further z-axis points within and / or around the object using interpolation. This may be repeated with multiple objects in field of view, including without limitation environmental features of interest identified by object classifier and / or indicated by an operator. In an embodiment, x and y axes may be chosen to span a plane common to two cameras used for stereoscopic image capturing and / or an xy-plane of a first frame; a result, x and y translational components and ϕ may be pre-populated in translational and rotational matrices, for affine transformation of 86 Attorney Docket No.1519-032PCT1 coordinates of object, also as described above. Initial x and y coordinates and / or guesses at transformational matrices may alternatively or additionally be performed between first frame and second frame, as described above. For each point of a plurality of points on object and / or edge and / or edges of object as described above, x and y coordinates of a first stereoscopic frame may be populated, with an initial estimate of z coordinates based, for instance, on assumptions about object, such as an assumption that ground is substantially parallel to an xy-plane as selected above. Z coordinates, and / or x, y, and z coordinates, registered using image capturing and / or object identification processes as described above may then be compared to coordinates predicted using initial guess at transformation matrices; an error function may be computed using by comparing the two sets of points, and new x, y, and / or z coordinates, may be iteratively estimated and compared until the error function drops below a threshold level. In some cases, a machine vision system may use a classifier, such as any classifier described throughout this disclosure. A z-axis, as used in this disclosure, is an axis that is orthogonal to the xy-plane and, thus, a top surface of slide 916. With continued reference to FIG.9A, optical system 920 may be configured to capture an image, such as images 936a-e of FIG.9B, of an area of interest 928. For example, and without limitation, a camera of optical system 920 may be configured to capture an image of an area of interest. For the purposes of this disclosure, an “area of interest” is a region of a scene or environment that is selected or desired to be positioned within a line of sight and, thus, a FOV 956 of an optical component of an optical system. An “line of sight,” for the purposes of this disclosure, is a line along which an observer or lens has unobstructed vision. A “field of view,” for the purposes of this disclosure, is an angle through and / or an area within which an optical component detects electromagnetic radiation. For instance, and without limitation, FOV may indicate an area of a scene that may be captured by an optical component within defined bounds (e.g., a frame) of an image. For example, and without limitation, area of interest 928 within FOV 956 of optical system 920 may include a scene desired to be captured in an image by being placed within a line of sight of a lens of optical system 920, so that image may be captured. FOV 956 may include vertical and horizontal angles that project relative to the surface of a lens of an optical component. In one or more embodiments, line of sight may include an optical access of the FOV. In various embodiments, an area of interest 928 may include at least a portion of 87 Attorney Docket No.1519-032PCT1 specimen 912. In some embodiments, an area of interest may include a portion of specimen 912 and a portion of slide 916. Still referring to FIG.9A, in one or more embodiments, image may include image data. As used in this disclosure, “image data” is information representing at least a physical scene, space, and / or object. Image data may include, for example, information representing a sample, slide 916, or region of a sample or slide. In some cases, image data may be generated by a camera. “Image data” may be used interchangeably through this disclosure with “image,” where image is used as a noun. An image may be optical, such as without limitation where at least an optic is used to generate an image of an object. An image may be digital, such as without limitation when represented as a bitmap. Alternatively, an image may be comprised of any media capable of representing a physical scene, space, and / or object. Alternatively, where “image” is used as a verb, in this disclosure, it refers to generation and / or formation of an image. Still referring to FIG.9A, in some embodiments, imaging device 900 may include a slide port 944. In some embodiments, slide port 944 may be configured to hold slide 916. In some embodiments, slide port 944 may include one or more alignment features. As used herein, an “alignment feature” is a physical feature that helps to secure a slide in place and / or align a slide with another component of an imaging device. In some embodiments, alignment feature may include a component which keeps slide 916 secure, such as a clamp, latch, clip, recessed area, or another fastener. In some embodiments, slide port 944 may allow for easy removal or insertion of slide 916. In some embodiments, slide port 944 may include a transparent surface through which light may travel. In some embodiments, slide 916 may rest on and / or may be illuminated by light traveling through such a transparent surface. In some embodiments, slide port 944 may be mechanically connected to an actuator mechanism 924 as described below. Still referring to FIG.9A, in some embodiments, imaging device 900 may include an actuator mechanism 924. As used herein, an “actuator mechanism” is a mechanical component configured to change the position of a slide relative to an optical system. In one or more embodiments, actuator mechanism may be used to alter line of sight so that an image of a new area of interest (e.g., a second area of interest) may be captured, as discussed further in this disclosure. In some embodiments, actuator mechanism 924 may be mechanically connected to slide 916, such as slide 916 in slide port 944. In some embodiments, actuator mechanism 924 may be mechanically connected to slide port 944. For example, actuator mechanism 924 may 88 Attorney Docket No.1519-032PCT1 move slide port 944 in order to move slide 916. For example, and without limitation, actuator mechanism 924 may move slide port 944 so that a distance D between a top surface of slide 916 and a lens of optical system 920 varies. In other embodiments, actuator mechanism 924 may also vary an angle between top surface (e.g., surface directed toward, or facing, optical system and that the specimen contacts) and lens. In some embodiments, actuator mechanism 924 may be mechanically connected to at least an optical system 920. In some embodiments, actuator mechanism 924 may be mechanically connected to a mobile element. As used in this disclosure, a “mobile element” refers to any movable or portable object, component, and device within imaging device 900 such as, without limitation, a slide, a slide port, or an optical system. In some embodiments, a mobile element may move such that optical system 920 is positioned correctly with respect to slide 916 such that optical system 920 may capture an image of slide 916 according to a parameter set. In some embodiments, actuator mechanism 924 may be mechanically connected to an item selected from the list consisting of slide port 944, slide 916, and at least an optical system 920. In some embodiments, actuator mechanism 924 may be configured to change the relative position of slide 916 and optical system 920 by moving slide port 944, slide 916, and / or optical system 920. Still referring to FIG.9A, actuator mechanism 924 may include a component of a machine that is responsible for moving and / or controlling a mechanism or system. Actuator mechanism 924 may, in some embodiments, require a control signal and / or a source of energy or power. In some cases, a control signal may be relatively low energy. Exemplary control signal forms include electric potential or current, pneumatic pressure or flow, or hydraulic fluid pressure or flow, mechanical force / torque or velocity, or even human power. In some cases, an actuator may have an energy or power source other than control signal. This may include a main energy source, which may include for example electric power, hydraulic power, pneumatic power, mechanical power, and the like. In some embodiments, upon receiving a control signal, actuator mechanism 924 responds by converting source power into mechanical motion. In some cases, actuator mechanism 924 may be understood as a form of automation or automatic control. Still referring to FIG.9A, in some embodiments, actuator mechanism 924 may include a hydraulic actuator. A hydraulic actuator may consist of a cylinder or fluid motor that uses hydraulic power to facilitate mechanical operation. Output of hydraulic actuator mechanism 924 may include mechanical motion, such as without limitation linear, rotatory, or oscillatory motion. 89 Attorney Docket No.1519-032PCT1 In some embodiments, hydraulic actuator may employ a liquid hydraulic fluid. As liquids, in some cases, are incompressible, a hydraulic actuator can exert large forces. Additionally, as force is equal to pressure multiplied by area, hydraulic actuators may act as force transformers with changes in area (e.g., cross sectional area of cylinder and / or piston). An exemplary hydraulic cylinder may consist of a hollow cylindrical tube within which a piston can slide. In some cases, a hydraulic cylinder may be considered single acting. “Single acting” may be used when fluid pressure is applied substantially to just one side of a piston. Consequently, a single acting piston can move in only one direction. In some cases, a spring may be used to give a single acting piston a return stroke. In some cases, a hydraulic cylinder may be double acting. “Double acting” may be used when pressure is applied substantially on each side of a piston; any difference in resultant force between the two sides of the piston causes the piston to move. Still referring to FIG.9A, in some embodiments, actuator mechanism 924 may include a pneumatic actuator mechanism. In some cases, a pneumatic actuator may enable considerable forces to be produced from relatively small changes in gas pressure. In some cases, a pneumatic actuator may respond more quickly than other types of actuators such as, for example, hydraulic actuators. A pneumatic actuator may use compressible fluid (e.g., air). In some cases, a pneumatic actuator may operate on compressed air. Operation of hydraulic and / or pneumatic actuators may include control of one or more valves, circuits, fluid pumps, and / or fluid manifolds. Still referring to FIG.9A, in some cases, actuator mechanism 924 may include an electric actuator. Electric actuator mechanism 924 may include any of electromechanical actuators, linear motors, and the like. In some cases, actuator mechanism 924 may include an electromechanical actuator. An electromechanical actuator may convert a rotational force of an electric rotary motor into a linear movement to generate a linear movement through a mechanism. Exemplary mechanisms, include rotational to translational motion transformers, such as without limitation a belt, a screw, a crank, a cam, a linkage, a scotch yoke, and the like. In some cases, control of an electromechanical actuator may include control of electric motor, for instance a control signal may control one or more electric motor parameters to control electromechanical actuator. Exemplary non-limitation electric motor parameters include rotational position, input torque, velocity, current, and potential. Electric actuator mechanism 924 may include a linear motor. Linear motors may differ from electromechanical actuators, as power from linear motors is 90 Attorney Docket No.1519-032PCT1 output directly as translational motion, rather than output as rotational motion and converted to translational motion. In some cases, a linear motor may cause lower friction losses than other devices. Linear motors may be further specified into at least 3 different categories, including flat linear motor, U-channel linear motors and tubular linear motors. Linear motors may be directly controlled by a control signal for controlling one or more linear motor parameters. Exemplary linear motor parameters include without limitation position, force, velocity, potential, and current. Still referring to FIG.9A, in some embodiments, an actuator mechanism 924 may include a mechanical actuator mechanism 924. In some cases, a mechanical actuator mechanism 924 may function to execute movement by converting one kind of motion, such as rotary motion, into another kind, such as linear motion. An exemplary mechanical actuator includes a rack and pinion. In some cases, a mechanical power source, such as a power take off may serve as power source for a mechanical actuator. Mechanical actuators may employ any number of mechanisms, including for example without limitation gears, rails, pulleys, cables, linkages, and the like. Still referring to FIG.9A, in some embodiments, actuator mechanism 924 may be in electronic communication with actuator controls. As used herein, “actuator controls” is a system configured to operate actuator mechanism such that a slide and an optical system reach a desired relative position. In some embodiments, actuator controls may operate actuator mechanism 924 based on input received from a user interface. In some embodiments, actuator controls may be configured to operate actuator mechanism 924 such that optical system 920 is in a position to capture an image of an entire sample. In some embodiments, actuator controls may be configured to operate actuator mechanism 924 such that optical system 920 is in a position to capture an image of a region of interest. As used in this disclosure, a “region of interest” (also referred to herein as an “area of interest”) is a specific area within a digital image, or a specific area within a slide. In some embodiments, a region of interest may include a region selected by a user or navigated to by a user. Electronic communication between actuator mechanism 924 and actuator controls may include transmission of signals. For example, actuator controls may generate physical movements of actuator mechanism in response to an input signal. In some embodiments, input signal may be received by actuator controls from processor 904 or input interface. 91 Attorney Docket No.1519-032PCT1 Still referring to FIG.9A, as used in this disclosure, a “signal” is any intelligible representation of data, for example from one device to another. A signal may include an optical signal, a hydraulic signal, a pneumatic signal, a mechanical signal, an electric signal, a digital signal, an analog signal, and the like. In one or more embodiments, image data may be transmitted through one or more signals. In other embodiments, instructions from an operator of imaging device 900 may send commands to a component of imaging device, such as optical system, through one or more signals. In some cases, a signal may be used to communicate with a computing device, for example, by way of one or more ports. In some cases, a signal may be transmitted and / or received by a computing device, for example by way of an input / output port. An analog signal may be digitized, for example by way of an analog to digital converter. In some cases, an analog signal may be processed, for example by way of any analog signal processing steps described in this disclosure, prior to digitization. In some cases, a digital signal may be used to communicate between two or more devices, including without limitation computing devices. In some cases, a digital signal may be communicated by way of one or more communication protocols, including without limitation internet protocol (IP), controller area network (CAN) protocols, serial communication protocols (e.g., universal asynchronous receiver-transmitter [UART]), parallel communication protocols (e.g., IEEE [printer port]), and the like. Still referring to FIG.9A, in some embodiments, imaging device 900 may perform one or more signal processing steps on a signal. For instance, imaging device 900 may analyze, modify, and / or synthesize a signal representative of data in order to improve the signal, for instance by improving transmission, storage efficiency, or signal to noise ratio. Exemplary methods of signal processing may include analog, continuous time, discrete, digital, nonlinear, and statistical. Analog signal processing may be performed on non-digitized or analog signals. Exemplary analog processes may include passive filters, active filters, additive mixers, integrators, delay lines, compandors, multipliers, voltage-controlled filters, voltage-controlled oscillators, and phase-locked loops. Continuous-time signal processing may be used, in some cases, to process signals which vary continuously within a domain, for instance time. Exemplary non-limiting continuous time processes may include time domain processing, frequency domain processing (Fourier transform), and complex frequency domain processing. Discrete time signal processing may be used when a signal is sampled non-continuously or at discrete time intervals (i.e., 92 Attorney Docket No.1519-032PCT1 quantized in time). Analog discrete-time signal processing may process a signal using the following exemplary circuits sample and hold circuits, analog time-division multiplexers, analog delay lines and analog feedback shift registers. Digital signal processing may be used to process digitized discrete-time sampled signals. Commonly, digital signal processing may be performed by a computing device or other specialized digital circuits, such as without limitation an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a specialized digital signal processor (DSP). Digital signal processing may be used to perform any combination of typical arithmetical operations, including fixed-point and floating-point, real- valued and complex-valued, multiplication and addition. Digital signal processing may additionally operate circular buffers and lookup tables. Further non-limiting examples of algorithms that may be performed according to digital signal processing techniques include fast Fourier transform (FFT), finite impulse response (FIR) filter, infinite impulse response (IIR) filter, and adaptive filters such as the Wiener and Kalman filters. Statistical signal processing may be used to process a signal as a random function (i.e., a stochastic process), utilizing statistical properties. For instance, in some embodiments, a signal may be modeled with a probability distribution indicating noise, which then may be used to reduce noise in a processed signal. Still referring to FIG.9A, in some embodiments, imaging device 900 may include a user interface, as previously described in this disclosure. User interface may include output interface and input interface. In some embodiments, output interface may include one or more elements through which imaging device 900 may communicate information to a user. In a non-limiting example, output interface may include a display. A display may include a high-resolution display. A display may output images, videos, and the like to a user. In another non-limiting example, output interface may include a speaker. A speaker may output audio to a user. In another non-limiting example, output interface may include a haptic device. A speaker may output haptic feedback to a user. Still referring to FIG.9A, in some embodiments, input interface may include controls for operating imaging device 900. Such controls may be operated by a user. Input interface may include, in non-limiting examples, a camera, microphone, keyboard, touch screen, mouse, joystick, foot pedal, button, dial, and the like. Input interface may accept, in non-limiting examples, mechanical input, audio input, visual input, text input, and the like. In some 93 Attorney Docket No.1519-032PCT1 embodiments, audio inputs into input interface may be interpreted using an automatic speech recognition function, allowing a user to control imaging device 900 via speech. In some embodiments, input interface may approximate controls of a microscope. Still referring to FIG.9A, in one or more embodiments, imaging device 900 may be configured to create multi-layer scan, where the multi-layer scan includes a plurality, such as a series, of images combined into a single image. A multi-layer scan may include an integrated image. For instance, and without limitation, multi-layer scan includes a compilation of consecutive images taken at different levels along a z-axis, or depth axis, at a particular location (x, y) of a specimen. For instance, and without limitation, multi-layer scan may include a plurality of images including an image taken with a focus depth A, an image taken with a focus depth B, and image taken with a focus depth C, and so on, as discussed further below. Now referring to FIG.9A, in various embodiments, imaging device 900 may receive one or more parameters from an operator of imaging device 900. One or more parameters may include a plurality of parameters (also referred to herein as a “parameter set”). As used in this disclosure, a “parameter set” is a set of values, such as without limitation quantitative and / or numerical values, that identify how an image is to be captured. Parameter set may be implemented as a data structure, as described below. In some embodiments, imaging device 900 may receive a parameter set from an operator using input interface. Parameter set may include x and y coordinates indicating a location on specimen that operator wishes to view (e.g., area of interest 928). For instance, and without limitation, imaging device 900 may receive parameters, such as a first location x associated with a first area of interest 928a and a second location x’ associated with a second area of interest 928b (shown in FIG.9B) from user input, database, or other means described in this disclosure. For instance, and without limitation, using user interface, operator may select first location x and second location x’ as parameters based on a field of vision (e.g., field of view) of optical component. In other embodiments, a trained machine-learning model may be used to determine first location x and second location x.’ For example, and without limitation, first location x and second location x’ may be determined using a field of vision required to cover a sample or a determination of items to be captured in the sample (e.g., specimen 912). Parameter set may include a desired focus depth and level of zoom. For the purposes of this disclosure, a “focus depth” (also referred to herein as a depth of focus”) is a distance between a lens, series of lenses, focal mirror, and / or series of focal mirrors and an 94 Attorney Docket No.1519-032PCT1 image sensor of optical system to achieve a specific focal point. As used in this disclosure, a “level of zoom” is a data related to a magnification of a region within a line of sight of an optical system. For instance, and without limitation, level of zoom may include a magnification of an area of interest within line of sight. A level of zoom may include optical zoom and / or digital zoom. As a non-limiting example, a level of zoom may be “8x” zoom. Parameter set may include a desired focus depth, as mentioned in this disclosure. In a non-limiting example, operator may manipulate input interface and / or imaging device 900 such that a parameter set includes x and y coordinates and a level of zoom corresponding to a more zoomed in view of a particular region of a sample. In some embodiments, parameter set corresponds to a more zoomed in view of a particular region of a sample that is included in first image. This may be done, for example, to get a more detailed view of at least a portion of specimen 912. As used in this disclosure, unless indicated otherwise, an “x coordinate” and a “y coordinate” refer to coordinates along perpendicular axes, where the plane defined by these axes is parallel to a plane of a surface of slide 916. In some cases, setting a level of zoom may include changing one or more optical elements of optical system 920. For example, and without limitation, setting a level of zoom may include replacing a first objective lens with a second objective lens having a different magnification. Additionally or alternative, one or more optical components “down beam” from objective lens may be replaced to change a total magnification of optical system and, thereby, set level of zoom. In some cases, setting level of zoom may include changing a digital magnification. Digital magnification may include outputting an image, using output interface, at a different resolution, i.e. after re-scaling the image. Still referring to FIG.9B, in some embodiments, imaging device 900 may be configured to determine one or more parameters of a first multi-layer scan 940a (also referred to herein as a “integrated image”) at first location x of specimen 912. First multi-layer scan 940a may include a plurality of first images 936a-e related to a first area of interest 928a of specimen 912 at first location x. For instance, and without limitation, first multi-layer scan 940a may include a first area of interest 928a out of a plurality of areas of interest 928a-c. In one or more embodiments, processor 904 of imaging device 900 may be configured to determine one or more parameters of first multi-layer scan 940a at first location x of specimen 912. In one or more embodiments, multi-layer scan may include a plurality of images, as shown in FIG.9B. In one or more embodiments, parameters may include measurable factors and / or conditions for taking an image 95 Attorney Docket No.1519-032PCT1 of a desired location of a specimen. Parameters of multi-layer scan may include a quantity of layers (e.g., one or more layers), each at a different focal plane and / or focal point of focal plane. A quantity of layers may include a plurality of layers and / or images. In a non-limiting exemplary embodiment, parameters of multi-layer scan 940a may include a first image 936a at a focal point A taken at a first location x as a first layer, a second image 936b at a focal point B as a second layer, a third image 936c at a focal point C as a third layer, a fourth image 936d at a focal point D as a fourth layer, a fifth image 936e at a focal point E as a fifth layer, and so on. In some embodiments, focal points are evenly spaced. In other embodiments, focal points may be various distanced from each other. Parameters of multi-layer scan may also include a size of each of layers and / or images. For instance, and without limitation, a size of each layer and / or image may include dimensions (units) of area of interest of specimen 912. Parameters of multi-layer scan may also include a quantity of layers, as previously mentioned. In one or more embodiments, each image may be dynamically captured, where images may include videos over a duration of time (e.g., temporal parameter). Still referring to FIG.9B, in some embodiments, determining one or more parameters of first multi-layer scan 940a at first location x of specimen 912 may include determining a count metric of first multi-image scan 940a. For the purposes of this disclosure, a “count metric” is a parameter related to a quantity. Count metric may include a quantity of layers, or images at varying depths, to be included in the multi-layer scan, such as plurality of images. In some embodiments, processor 904 may receive one or more count metrics from operator. In other embodiments, processor 904 may determine count metrics. In one or more embodiments, the quantity of layers may be identified by estimating a depth (e.g., thickness) of specimen 912. In one or more non-limiting embodiments, depth of specimen 912 may be identified by subtracting a thickness Ts of slide 916 that specimen is disposed on and a thickness Tc of a coverslip from the combined thickness TT of the slide and the cover slip, i.e., Ds = TT-(Ts+Tc). The depth Ds of the specimen may then be divided into spacings (i.e. steps per layer) at specific depths of field and objectives. In some embodiments, each image of the plurality of images may have a different focal point. The focus distance between each image may be controlled using imaging device, using, for example, a motor to horizontally and / or vertically move slide relative to a lens of the imaging device, such as actuator mechanism 924. In other embodiments, detecting one or more parameters may include detecting a thickness t of specimen at an area of interest. For instance, 96 Attorney Docket No.1519-032PCT1 and without limitation, a light sensor, pressure sensor, infrared sensor, and the like may be used to determine a height or thickness of specimen 912 at any location along specimen 912. Still referring to FIG.9B, determining one or more parameters of first multi-layer scan 940a at first location x of specimen 912 may include determining a focus metric of each layer of the first multi-layer scan. A focal point, such as focal points A, B, C, D, and E of FIG.9B, may be determined as a function of focus metrics. Focal point may be adjusted by moving specimen 912 relative to the lens of the imaging device along a z-axis, such as for example, using actuator mechanism 924. Focus metrics of each layer may be determined based on creating focus planes where at least a portion of specimen 912 is in focus in a chosen focus plane. Still referring to FIG.9B, in one or more embodiments, imaging device 900 may be configured to capture a first plurality of images 936 of specimen at a first location x. In various embodiments, optical system 920 may capture at least a first image 936a of specimen 912 at a first location x of specimen 912 of a first area of interest 928a. For instance, and without limitation, optical system 920 may capture first plurality of images 936 of first area of interest 928a while first area of interest 928a is positioned within line of sight 932 of optical system 920. In some embodiments, capturing first plurality of images 936a of first area of interest 928a while slide 916 is in a first position may include using actuator mechanism 924 and / or actuator controls to move optical system 920 and / or slide 916 into a desired position. Desired position may include a location and / or orientation of slide 916 and / or specimen 912 within a space defined by, for example and without limitation, a Cartesian coordinate system (x, y, z), polar coordinate system (r, θ), cylindrical coordinate system (ρ, φ, z), or spherical coordinate system (r, θ, φ). In some embodiments, each image 936a-e of first plurality of images may include an image of the entire sample and / or the entire slide 916. In other embodiments, each image 936a-e of first plurality of images may include an image of a region of a sample. In some embodiments, one image of first plurality of images may include a wider angle image another image of first plurality of images. In some embodiments, one image of first plurality of images may include a lower resolution image than another image. In some embodiments, a machine vision system and / or an optical character recognition system may be used to determine one or more features of sample and / or slide 916. Such feature determinations may be used to, for example, remove artifacts from images and / or annotate images as described below. In a non-limiting example, an optical character recognition system may be used to identify writing on slide 916, and this may 97 Attorney Docket No.1519-032PCT1 be used to annotate an image of slide 916. In another non-limiting example, a machine vision system may be used to detect dust particles in a slide 916, and an artifact removal machine- learning model (described below) may remove the dust particles from the image. Still referring to FIG.9B, first plurality of images 936 may include one or more images 936a-e of the same x and y coordinates at differing focus depths along the z-axis. As used in this disclosure, a “focus depth” is a depth at which at least a portion of specimen is in focus for optical system. Focus depth may include the depth at which a specific focal point is achieved. As used in this disclosure, a “focus distance” is a distance f from optical system at which optical sensor is in focus. For instance, and without limitation, first image 936a and second image 936b of first plurality of images 936 may have different focus distances and / or focus depths. In some embodiments, such images may be stored as a plurality of layers of a multi-layer scan. For example, and without limitation, each image may include a layer of first plurality of images corresponding with a particular focus depth. In some embodiments, an operator may be able to navigate between layers using a user interface, such as a user interface of a computing system or remote device communicatively connected to processor 904 and memory 908. In a non-limiting example, this may be implemented by displaying to a user a focus depth selection menu. In this example, user may select a first focus depth, and an image taken at first focus depth may be displayed. In this example, an operator may then select a second focus depth, and an image taken at second focus depth may be displayed. In some embodiments, imaging device 900 may capture images at a particular focus depth upon selection of that focus depth by a user. Still referring to FIG.9B, in one or more embodiments, imaging device 900 may be configured to compile first plurality of images 936 into a first integrated image 940a. For the purposes of this disclosure, an “integrated image” is a processed multi-layer scan composed of a plurality of images taken at various focus depths. In one or more embodiments, multi-layer scan may include a plurality of overlayed images. For instance, and without limitation, plurality of images 936a-e captured at first location x may be consolidated to create first integrated image 940a. In one or more embodiments, integrated image may include an extended field of depth (EFOD) image. In one or more embodiments, first plurality of images 938a-e may be compiled together to create first integrate image 940a using an image processing module of imaging device 900. 98 Attorney Docket No.1519-032PCT1 With continued reference to FIG.9B, imaging device 900 may combine first plurality of images 936 by registering pixels to one another. For instance, and without limitation, processor 904 of imaging device 900 may determine which pixels in each image 936a-e are associated so that each image 936a-e of the first plurality of images 936 may be adjusted accordingly (e.g., each pixel of images 936a-e are aligned or have similar treatment, such as chroma, luma, white balance, or other values). With continued reference to FIG.9B, imaging device 900 may combine first plurality of images 936 through image fusion. As used in this disclosure, “image fusion” is a process of gathering important information from multiple images into fewer, usually one image. One method of image fusion includes multi-focus image fusion. As used in this disclosure, “multi- focus image fusion” is image fusion process that combines input images having different focus depths to create at least an output image comprising at focus image data from the input images. According to some embodiments, multi-focus image fusion may include selection of at focus image data from multiple input images. Determination of at focus image data may be performed on a per pixel basis, for example according to one or more image fusion metrics. Generally, image fusion may be categorized according to two categories: transform and spatial domains. Commonly used transforms for image fusion are Discrete cosine transform (DCT) and Multi- Scale Transform (MST). In some cases, image fusion may be based on MST. Exemplary MST methods include Laplacian pyramid transform, gradient pyramid-based transform, morphological pyramid transform and the premier ones, discrete wavelet transform, shift-invariant wavelet transform (SIDWT), and discrete cosine harmonic wavelet transform (DCHWT). In some cases, DCT-based methods may be more efficient in terms of transmission and archiving images compared with MST. In some cases, DCT may be employed on images encoded in Joint Photographic Experts Group (JPEG). A JPEG system consists of a pair of an encoder and a decoder. In the encoder, images are divided into non-overlapping 8×8 blocks, and the DCT coefficients are calculated for each. Since the quantization of DCT coefficients is a lossy process, many of the small-valued DCT coefficients are quantized to zero, which corresponds to high frequencies. DCT-based image fusion algorithms work better when the multi-focus image fusion methods are applied in the compressed domain. In some cases, DCT domain-based image fusion methods do not require consecutive decoding and encoding operations. Exemplary DCT image fusion processes include DCT+Variance, DCT+Corr_Eng, DCT+EOL, and DCT+VOL. Image 99 Attorney Docket No.1519-032PCT1 fusion methods may additionally or alternatively include processes in the spatial domain. Image fusion may employ determination and use of focus measurements including variance, energy of image gradient (EOG), Tenenbaum's algorithm (Tenengrad), energy of Laplacian (EOL), sum- modified-Laplacian (SML), and spatial frequency (SF). Image fusion may include aggregating in focus regions from multiple input images into an image. In some cases, boundaries, either at an edge of an image or between regions having different in focus input images may be processed differently. For instance, in some cases, section of an image at a boundary between two (or more) in focus images may be processed as a weighted average of values from the two (or more) nearest in focus images. In some cases, machine learning may be used to aid in image fusion processes. With continued reference to FIG.9B, imaging device 900 may combine first plurality of images 936 using a machine learning model, training a machine-learning model such as without limitation a convolutional neural net, deep neural net or some combination thereof to generate an integrated image given two or more images as inputs. Two-image input could be done iteratively with one or both of the two images a previous output of the neural network. With continued reference to FIG.9B, imaging device 900 may include an image processing module. As used in this disclosure, an “image processing module” is a component designed to process digital images. For example, and without limitation, image processing, module may be configured to compile plurality of images of a multi-layer scan to create an integrated image. In an embodiment, image processing module may include a plurality of software algorithms that can analyze, manipulate, or otherwise enhance an image, such as, without limitation, a plurality of image processing techniques as described below. In another embodiment, image processing module may slow include hardware components such as, without limitation, one or more graphics processing units (GPUs) that can accelerate the processing of large amount of images. In some cases, image processing module may be implemented with one or more image processing libraries such as, without limitation, OpenCV, PIL / Pillow, ImageMagick, and the like. Image processing module may include, be included in, or be communicatively connected to optical system 920, processor 904, and / or memory 908. Still referring to FIG.9B, image processing module may be configured to receive images from optical system 920. In a non-limiting example, image processing module may be configured to receive images by generating a first image capture parameter, transmitting a 100 Attorney Docket No.1519-032PCT1 command to optical system to take first image of a plurality of images with the first image capture parameter, generate a second image capture parameter, transmit a command to optical system to take second image of a plurality of images with the second image capture parameter, and receive, from optical system, first image and second image. In another non-limiting example, plurality of images may be taken by optical system using the same image capture parameter. Image capture parameter may be generated as a function of user input or processor 904. Still referring to FIG.9B, plurality of images may be transmitted from optical system 920 to image processing module via any suitable electronic communication protocol, including without limitation packet-based protocols such as transfer control protocol-internet protocol (TCP-IP), file transfer protocol (FTP) or the like. Receiving images may include retrieval of images from a data store containing images as described below; for instance, and without limitation, images may be retrieved using a query that specifies a timestamp that images may be required to match. Still referring to FIG.9B, image processing module may be configured to process images. In an embodiment, image processing module may be configured to compress and / or encode images to reduce the file size and storage requirements while maintaining the essential visual information needed for further processing steps as described below. In an embodiment, compression and / or encoding of plurality of images may facilitate faster transmission of images. In some cases, image processing module may be configured to perform a lossless compression on images, wherein the lossless compression may maintain the original image quality of images. In a non-limiting example, image processing module may utilize one or more lossless compression algorithms, such as, without limitation, Huffman coding, Lempel-Ziv-Welch (LZW), Run-Length Encoding (RLE), and / or the like to identify and remove redundancy in each image in a plurality of images without losing any information. In such embodiment, compressing and / or encoding each image of a plurality of images may include converting the file format of each image into PNG, GIF, lossless JPEG2000 or the like. In an embodiment, images compressed via lossless compression may be perfectly reconstructed to the original form (e.g., original image resolution, dimension, color representation, format, and the like) of images. In other cases, image processing module may be configured to perform a lossy compression on plurality of images, wherein the lossy compression may sacrifice some image quality of images to achieve higher compression ratios. In a non-limiting example, image processing module may 101 Attorney Docket No.1519-032PCT1 utilize one or more lossy compression algorithms, such as, without limitation, Discrete Cosine Transform (DCT) in JPEG or Wavelet Transform in JPEG2000, discard some less significant information within images, resulting in a smaller file size but a slight loss of image quality of images. In such embodiment, compressing and / or encoding each image of a plurality of images may include converting the file format of each image into JPEG, WebP, lossy JPEG2000, or the like. Still referring to FIG.9B, in an embodiment, processing images may include determining a degree of quality of depiction of a region of interest of an image or a plurality of images. In an embodiment, image processing module may determine a degree of blurriness of images. In a non-limiting example, image processing module may perform a blur detection by taking a Fourier transform, or an approximation such as a Fast Fourier Transform (FFT) of images and analyzing a distribution of low and high frequencies in the resulting frequency-domain depiction of images; for instance, and without limitation, numbers of high-frequency values below a threshold level may indicate blurriness. In another non-limiting example, detection of blurriness may be performed by convolving images, a channel of images, or the like with a Laplacian kernel; for instance, and without limitation, this may generate a numerical score reflecting a number of rapid changes in intensity shown in each image, such that a high score indicates clarity and a low score indicates blurriness. In some cases, blurriness detection may be performed using a Gradient-based operator, which measures operators based on the gradient or first derivative of images, based on the hypothesis that rapid changes indicate sharp edges in the image, and thus are indicative of a lower degree of blurriness. In some cases, blur detection may be performed using Wavelet-based operator, which takes advantage of the capability of coefficients of the discrete wavelet transform to describe the frequency and spatial content of images. In some cases, blur detection may be performed using statistics-based operators take advantage of several image statistics as texture descriptors in order to compute a focus level. In other cases, blur detection may be performed by using discrete cosine transform (DCT) coefficients in order to compute a focus level of images from its frequency content. Additionally, or alternatively, image processing module may be configured to rank images according to degree of quality of depiction of a region of interest and select a highest-ranking image from a plurality of images. 102 Attorney Docket No.1519-032PCT1 Still referring to FIG.9B, processing images may include enhancing at least a region of interest via a plurality of image processing techniques to improve the quality (or degree of quality of depiction) of an image for better processing and analysis as described further in this disclosure. In an embodiment, image processing module may be configured to perform a noise reduction operation on an image, wherein the noise reduction operation may remove or minimize noise (arises from various sources, such as sensor limitations, poor lighting conditions, image compression, and / or the like), resulting in a cleaner and more visually coherent image. In some cases, noise reduction operation may be performed using one or more image filters; for instance, and without limitation, noise reduction operation may include Gaussian filtering, median filtering, bilateral filtering, and / or the like. Noise reduction operation may be done by image processing module, by averaging or filtering out pixel values in neighborhood of each pixel of an image to reduce random variations. Still referring to FIG.9B, in another embodiment, image processing module may be configured to perform a contrast enhancement operation on an image. In some cases, an image may exhibit low contrast, which may, for example, make a feature difficult to distinguish from the background. Contrast enhancement operation may improve the contrast of an image by stretching the intensity range of the image and / or redistributing the intensity values (i.e., degree of brightness or darkness of a pixel in the image). In a non-limiting example, intensity value may represent the gray level or color of each pixel, scale from 0 to 255 in intensity range for an 8-bit image, and scale from 0 to 16,777,215 in a 24-bit color image. In some cases, contrast enhancement operation may include, without limitation, histogram equalization, adaptive histogram equalization (CLAHE), contrast stretching, and / or the like. Image processing module may be configured to adjust the brightness and darkness levels within an image to make a feature more distinguishable (i.e., increase degree of quality of depiction). Additionally, or alternatively, image processing module may be configured to perform a brightness normalization operation to correct variations in lighting conditions (i.e., uneven brightness levels). In some cases, an image may include a consistent brightness level across a region after brightness normalization operation performed by image processing module. In a non-limiting example, image processing module may perform a global or local mean normalization, where the average intensity value of an entire image or region of an image may be calculated and used to adjust the brightness levels. 103 Attorney Docket No.1519-032PCT1 Still referring to FIG.9B, in other embodiments, image processing module may be configured to perform a color space conversion operation to increase degree of quality of depiction. In a non-limiting example, in case of a color image (i.e., RGB image), image processing module may be configured to convert RGB image to grayscale or HSV color space. Such conversion may emphasize the differences in intensity values between a region or feature of interest and the background. Image processing module may further be configured to perform an image sharpening operation such as, without limitation, unsharp masking, Laplacian sharpening, high-pass filtering, and / or the like. Image processing module may use image sharpening operation to enhance the edges and fine details related to a region or feature of interest within an image by emphasizing high-frequency components within an image. Still referring to FIG.9B, processing images may include isolating a region or feature of interest from the rest of an image as a function of plurality of image processing techniques. Images may include highest-ranking image selected by image processing module as described above. In an embodiment, plurality of image processing techniques may include one or more morphological operations, wherein the morphological operations are techniques developed based on set theory, lattice theory, topology, and random functions used for processing geometrical structures using a structuring element. A “structuring element,” for the purpose of this disclosure, is a small matrix or kernel that defines a shape and size of a morphological operation. In some cases, structing element may be centered at each pixel of an image and used to determine an output pixel value for that location. In a non-limiting example, isolating a region or feature of interest from an image may include applying a dilation operation, wherein the dilation operation is a basic morphological operation configured to expand or grow the boundaries of objects (e.g., a cell, a dust particle, and the like) in an image. In another non-limiting example, isolating a region or feature of interest from an image may include applying an erosion operation, wherein the erosion operation is a basic morphological operation configured to shrink or erode the boundaries of objects in an image. In another non-limiting example, isolating a region or feature of interest from an image may include applying an opening operation, wherein the opening operation is a basic morphological operation configured to remove small objects or thin structures from an image while preserving larger structures. In a further non-limiting example, isolating a region or feature of interest from an image may include applying a closing operation, wherein the closing operation is a basic morphological operation configured to fill in small gaps 104 Attorney Docket No.1519-032PCT1 or holes in objects in an image while preserving the overall shape and size of the objects. These morphological operations may be performed by image processing module to enhance the edges of objects, remove noise, or fill gaps in a region or feature of interest before further processing. Still referring to FIG.9B, in an embodiment, isolating a region or feature of interest from an image may include utilizing an edge detection technique, which may detect one or more shapes defined by edges. An “edge detection technique,” as used in this disclosure, includes a mathematical method that identifies points in a digital image, at which the image brightness changes sharply and / or has a discontinuity. In an embodiment, such points may be organized into straight and / or curved line segments, which may be referred to as “edges.” Edge detection technique may be performed by image processing module, using any suitable edge detection algorithm, including without limitation Canny edge detection, Sobel operator edge detection, Prewitt operator edge detection, Laplacian operator edge detection, and / or Differential edge detection. Edge detection technique may include phase congruency-based edge detection, which finds all locations of an image where all sinusoids in the frequency domain, for instance as generated using a Fourier decomposition, may have matching phases which may indicate a location of an edge. Edge detection technique may be used to detect a shape of a feature of interest such as a cell, indicating a cell membrane or wall; in an embodiment, edge detection technique may be used to find closed figures formed by edges. Still referring to FIG.9B, in a non-limiting example, isolating a feature of interest from an image may include determining a feature of interest via edge detection technique. A feature of interest may include a specific area within a digital image that contains information relevant to further processing as described below. In a non-limiting example, an image data located outside a feature of interest may include irrelevant or extraneous information. Such portion of an image containing irrelevant or extraneous information may be disregarded by image processing module, thereby allowing resources to be concentrated at a feature of interest. In some cases, feature of interest may vary in size, shape, and / or location within an image. In a non-limiting example feature of interest may be presented as a circle around the nucleus of a cell. In some cases, feature of interest may specify one or more coordinates, distances and the like, such as center and radius of a circle around the nucleus of a cell in an image. Image processing module may then be configured to isolate feature of interest from the image based on feature of interest. In a non- 105 Attorney Docket No.1519-032PCT1 limiting example, image processing module may crop an image according to a bounding box around a feature of interest. Still referring to FIG.9B, image processing module may be configured to perform a connected component analysis (CCA) on an image for feature of interest isolation. As used in this disclosure, a “connected component analysis (CCA),” also known as connected component labeling, is an image processing technique used to identify and label connected regions within a binary image (i.e., an image which each pixel having only two possible values: 0 or 9, black or white, or foreground and background). “Connected regions,” as described herein, is a group of adjacent pixels that share the same value and are connected based on a predefined neighborhood system such as, without limitation, 4-connected or 8-connected neighborhoods. In some cases, image processing module may convert an image into a binary image via a thresholding process, wherein the thresholding process may involve setting a threshold value that separates the pixels of an image corresponding to feature of interest (foreground) from those corresponding to the background. Pixels with intensity values above the threshold may be set to 1 (white) and those below the threshold may be set to 0 (black). In an embodiment, CCA may be employed to detect and extract feature of interest by identifying a plurality of connected regions that exhibit specific properties or characteristics of the feature of interest. Image processing module may then filter plurality of connected regions by analyzing plurality of connected regions properties such as, without limitation, area, aspect ratio, height, width, perimeter, and / or the like. In a non-limiting example, connected components that closely resemble the dimensions and aspect ratio of feature of interest may be retained, by image processing module as feature of interest, while other components may be discarded. Image processing module may be further configured to extract feature of interest from an image for further processing as described below. Still referring to FIG.9B, in an embodiment, isolating feature of interest from an image may include segmenting a region depicting a feature of interest into a plurality sub-regions. Segmenting a region into sub-regions may include segmenting a region as a function of feature of interest and / or CCA via an image segmentation process. As used in this disclosure, an “image segmentation process” is a process for partition a digital image into one or more segments, where each segment represents a distinct part of the image. Image segmentation process may change the representation of images. Image segmentation process may be performed by image processing module. In a non-limiting example, image processing module may perform a region- 106 Attorney Docket No.1519-032PCT1 based segmentation, wherein the region-based segmentation involves growing regions from one or more seed points or pixels on an image based on a similarity criterion. Similarity criterion may include, without limitation, color, intensity, texture, and / or the like. In a non-limiting example, region-based segmentation may include region growing, region merging, watershed algorithms, and the like. Still referring to FIG.9B, in some embodiments, imaging device 900 may remove an artifact identified by machine vision system or an optical character recognition system, which are described above. Non-limiting examples of artifacts that may be removed include dust particles, bubbles, cracks in slide 916, writing on slide 916, shadows, visual noise such as in a grainy image, and the like. In some embodiments, an artifact may be partially removed and / or lowered in visibility. Still referring to FIG.9B, in some embodiments, an artifact may be removed using an artifact removal machine learning mode. In some embodiments, artifact removal machine learning model may be trained on a dataset including images, associated with images without artifacts. In some embodiments, artifact removal machine learning model may accept as an input an image including an artifact and may output an image without the artifact. For example, artifact removal machine learning model may accept as an input an image including a bubble in a slide and may output an image that does not include the bubble. In some embodiments, artifact removal machine learning model may include a generative machine learning model such as a diffusion model. A diffusion model may learn the structure of a dataset by modeling the way data points diffuse through a latent space. In some embodiments, artifact removal may be done locally. For example, and without limitation, imaging device 900 may include an already trained artifact removal machine learning model and may apply the model to an image. In some embodiments, artifact removal may be done externally. For example, imaging device 900 may transmit image data to another computing device and may receive an image with an artifact removed. In some embodiments, an artifact may be removed in real time. In some embodiments, an artifact may be removed based on identification by a user. For example, a user may drag a box around an artifact using a mouse cursor, and imaging device 900 may remove an artifact in the box. Still referring to FIG.9B, in some embodiments, imaging device 900 may remove an artifact from first integrated image 940a or any of first plurality of images prior to compilation. 107 Attorney Docket No.1519-032PCT1 For instance, in some embodiments, imaging device 900 may remove an artifact from a second image 936b. In some embodiments, imaging device 900 may remove an artifact from a hybrid image, such as first integrated image 940a. Still referring to FIG.9B, in one or more embodiments, imaging device 900 may be configured to move slide port 944, slide 916, one or more components of optical system 920, or the like into a second position, wherein second location x’ of specimen is within line of sight 932 of optical system 920. Second position may be based on one or more parameters, such as an original parameter set for first plurality of images or an updated parameter set. A new position may allow for a new location to be placed within the FOV 956 of optical component so that a different plurality of images from the previous plurality of images (e.g., z-stack) may be captured by imaging system. Second position may include, for example and without limitation, modifying a current position (e.g., first position) of optical system 920 relative to slide 916 and specimen 912, or vice versa, as a function of parameter set, as indicated by the directional arrows of FIG. 9B. For instance, and without limitation, first location x may include a first area of interest 928a located at (x, y), and second location x’ may include a second area of interest 928b located at (x’, y’). Second location x’ may differ from first location x by, for example, a variance in an x and / or y coordinate value, and may be placed in line of sight 932 by a transition from first position to second position. For example, and without limitation, parameter set may indicate that second position is achieved by moving slide 916, for example, 5mm in a particular direction, such as along the x-axis. Similarly, in another example, and without limitation, second position may be found by modifying optical system’s original position by 5mm in an opposing direction. In some embodiments, such movement may be done using actuator mechanism 924. In some embodiments, actuator mechanism 924 may move slide port 944 such that slide 916 is in a position relative to at least an optical system 920...

Claims

WHAT IS CLAIMED IS:

1. An apparatus for real time image generation, the apparatus comprising: at least an optical system; a slide port configured to hold a slide; an actuator mechanism mechanically connected to a mobile element; a user interface comprising an input interface and an output interface; at least a processor; and a memory communicatively connected to the at least processor, the memory containing instructions configuring the at least processor to: using the at least an optical system, capture a first image of the slide at a first position; modify the first image; using the output interface, display the first image to a user; using the input interface, receive a parameter set from the user; using the actuator mechanism, move the mobile element into a second position, wherein the second position is determined based on the parameter set; using the at least an optical system, capture a second image of the slide at the second position; modify the second image; and using the output interface, display the second image to the user.

2. The apparatus of claim 1, wherein moving the mobile element into the second position comprises: positioning, using the actuator mechanism, the slide relative to the at least an optical system such that a region of interest defined by the parameter set is within a frame of at least an optical sensor of the at least an optical system; and setting a zoom of the at least an optical system based on a level of zoom specified by the parameter set.

3. The apparatus of claim 1, wherein moving the mobile element into a second position comprises setting a focus height of the at least an optical system based on a focus height specified by the parameter set. 143 Attorney Docket No.1519-032PCT14. The apparatus of claim 1, wherein moving the mobile element into a second position comprises: detecting a sample height at a region of interest; and setting a focus height of the at least an optical system based on the sample height at the region of interest.

5. The apparatus of claim 1, wherein the second image is captured using a higher level of zoom than that used to capture the first image.

6. The apparatus of claim 5, wherein modifying the second image comprises: replacing at least a region of the first image with the second image to create a hybrid second image; and displaying the hybrid second image to the user.

7. The apparatus of claim 6, wherein modifying the first image comprises: receiving, using the user interface, an annotation instruction from the user; and adding an annotation to the first image based on the annotation instruction.

8. The apparatus of claim 7, wherein the memory contains instructions configuring the at least a processor to: train an annotation machine learning model using a training dataset including image data associated with a feature depicted by the image; input into the annotation machine learning model an image selected from the list consisting of the first image, the second image, and the hybrid image; receive from the annotation machine learning model a datum identifying a feature depicted by the image selected from the list consisting of the first image, the second image, and the hybrid image; and add an annotation to the first image, as a function of the image feature.

9. The apparatus of claim 1, wherein modifying the first image comprises: removing a first artifact from the first image, wherein removing the first artifact comprises: inputting the first image into an artifact removal machine learning model; and receiving, from the artifact removal machine learning model, a version of first image lacking the artifact.

10. The apparatus of claim 9, wherein modifying the second image comprises: 144 Attorney Docket No.1519-032PCT1removing a second artifact from the second image, wherein removing the second artifact comprises: inputting the second image into an artifact removal machine learning model; and receiving from the artifact removal machine learning model a version of second image lacking the artifact.

11. A method of real time image generation, the method comprising: using at least a processor and at least an optical system, capturing a first image of a slide at a first position; using the at least a processor, modify the first image; using the at least a processor and an output interface, displaying the first image to a user; using the at least a processor and an input interface, receiving a parameter set from the user; using the at least a processor and an actuator mechanism, moving a mobile element into a second position, wherein the second position is determined based on the parameter set; using the at least a processor and the at least an optical system, capturing a second image of the slide at the second position; using the at least a processor, modify the first image; and using the at least a processor and the output interface, displaying the second image to the user.

12. The method of claim 11, wherein moving the mobile element into the second position comprises: positioning, using the actuator mechanism, the slide relative to the at least an optical system such that a region of interest defined by the parameter set is within a frame of at least an optical sensor of the at least an optical system; and setting a zoom of the at least an optical system based on a level of zoom specified by the parameter set.

13. The method of claim 11, wherein moving the mobile element into a second position comprises setting a focus height of the at least an optical system based on a focus height specified by the parameter set. 145 Attorney Docket No.1519-032PCT114. The method of claim 11, wherein moving the mobile element into a second position comprises: detecting a sample height at a region of interest; and setting a focus height of the at least an optical system based on the sample height at the region of interest.

15. The method of claim 11, wherein the second image is captured using a higher level of zoom than that used to capture the first image.

16. The method of claim 15, wherein modifying the second image comprises: replacing at least a region of the first image with the second image to create a hybrid second image; and displaying the hybrid second image to the user.

17. The method of claim 16, wherein modifying the first image comprises: receiving, using the user interface, an annotation instruction from the user; and adding an annotation to an image selected from to the first image, based on the annotation instruction.

18. The method of claim 16, wherein modifying the first image comprises: training an annotation machine learning model using a training dataset including image data associated with a feature depicted by the image; inputting into the annotation machine learning model an image selected from the list consisting of the first image, the second image, and the hybrid second image; receiving from the annotation machine learning model a datum identifying a feature depicted by the image selected from the list consisting of the first image, the second image, and the hybrid second image; and adding an annotation to the first image, as a function of the image feature.

19. The method of claim 11, wherein modifying the first image comprises: removing a first artifact from the first image, wherein removing the first artifact comprises: inputting the first image into an artifact removal machine learning model; and receiving, from the artifact removal machine learning model, a version of first image lacking the artifact.

20. The method of claim 11, wherein modifying the second image comprises: 146 Attorney Docket No.1519-032PCT1removing a second artifact from the second image, wherein removing the second artifact comprises: inputting the second image into an artifact removal machine learning model; and receiving from the artifact removal machine learning model a version of second image lacking the artifact.

21. An apparatus for real time image generation, the apparatus comprising: at least an optical system; a slide port configured to hold a slide; an actuator mechanism mechanically connected to a mobile element; a user interface comprising an input interface and an output interface, wherein the input interface is configured to receive a parameter set from a user; at least a processor; and a memory communicatively connected to the at least processor, the memory containing instructions configuring the at least processor to: using the at least an optical system, capture a first image of the slide at a first position; modify the first image; using the output interface, display the modified first image to the user; using the actuator mechanism, move the mobile element into a second position, wherein the second position is determined based on the parameter set.

22. The apparatus of claim 21, wherein moving the mobile element into the second position comprises: positioning, using the actuator mechanism, the slide relative to the at least an optical system such that a region of interest defined by the parameter set is within a frame of at least an optical sensor of the at least an optical system; and setting a zoom of the at least an optical system based on a level of zoom specified by the parameter set.

23. The apparatus of claim 21, wherein the input from the user comprises an audio input, and wherein the audio input is interpreted using an automatic speech recognition function.

24. The apparatus of claim 21, wherein the parameter set comprises an image capture parameter, and wherein the first image is captured using the image capture parameter. 147 Attorney Docket No.1519-032PCT125. The apparatus of claim 21, wherein the memory contains instructions configuring the at least processor to transmit the first image to an image processing module, wherein the image processing module is configured to determine a region of interest is by isolating a feature of interest from the first image and segmenting the region of interest depicting a feature of interest into a plurality of sub-regions.

26. The apparatus of claim 25, wherein the memory contains instructions configuring the at least processor to determine a degree of quality of depiction of the region of interest of the first image.

27. The apparatus of claim 21, wherein modifying the first image comprises: receiving, using the user interface, an annotation instruction from the user; and adding an annotation to the first image based on the annotation instruction.

28. The apparatus of claim 27, wherein the memory contains instructions configuring the at least a processor to: train an annotation machine learning model using a training dataset including image data associated with a feature depicted by the image; input into the annotation machine learning model an image selected from the list consisting of the first image, the second image, and a hybrid image; receive from the annotation machine learning model a datum identifying a feature depicted by the image selected from the list consisting of the first image, the second image, and the hybrid image; and add an annotation to the first image, as a function of the image feature.

29. The apparatus of claim 21, wherein modifying the first image comprises: removing a first artifact from the first image, wherein removing the first artifact comprises: inputting the first image into an artifact removal machine learning model; and receiving, from the artifact removal machine learning model, a version of first image lacking the artifact.

30. The apparatus of claim 21, wherein modifying the first image comprises isolating a feature of interest from an image and determining a feature of interest using an edge detection technique.

31. A method of real time image generation, the method comprising: 148 Attorney Docket No.1519-032PCT1using at least a processor and at least an optical system, capturing a first image of a slide at a first position; using the at least a processor, modifying the first image; using the at least a processor and an input interface, receiving a parameter set from a user; using the at least a processor and an actuator mechanism, moving a mobile element into a second position, wherein the second position is determined based on the parameter set; and using an output interface, displaying the modified first image to a user.

32. The method of claim 31, wherein moving the mobile element into the second position comprises: positioning, using the actuator mechanism, the slide relative to the at least an optical system such that a region of interest defined by the parameter set is within a frame of at least an optical sensor of the at least an optical system; and setting a zoom of the at least an optical system based on a level of zoom specified by the parameter set.

33. The method of claim 31, wherein the input from the user comprises an audio input, and wherein the audio input is interpreted using an automatic speech recognition function.

34. The method of claim 31, wherein the parameter set comprises an image capture parameter, and wherein the first image is captured using the image capture parameter.

35. The method of claim 31, further comprising: using the at least a processor, transmitting the first image to an image processing module, wherein the image processing module is configured to determine a region of interest is by isolating a feature of interest from the first image and segmenting the region of interest depicting a feature of interest into a plurality of sub-regions.

36. The method of claim 35, further comprising: using the at least a processor, determining a degree of quality of depiction of the region of interest of the first image 37. The method of claim 31, wherein modifying the first image comprises: receiving, using the user interface, an annotation instruction from the user; and 149 Attorney Docket No.1519-032PCT1adding an annotation to an image selected from to the first image, based on the annotation instruction.

38. The method of claim 37, wherein modifying the first image comprises: training an annotation machine learning model using a training dataset including image data associated with a feature depicted by the image; inputting into the annotation machine learning model an image selected from the list consisting of the first image, the second image, and a hybrid second image; receiving from the annotation machine learning model a datum identifying a feature depicted by the image selected from the list consisting of the first image, the second image, and the hybrid second image; and adding an annotation to the first image, as a function of the image feature.

39. The method of claim 31, wherein modifying the first image comprises: removing a first artifact from the first image, wherein removing the first artifact comprises: inputting the first image into an artifact removal machine learning model; and receiving, from the artifact removal machine learning model, a version of first image lacking the artifact.

40. The method of claim 31, wherein modifying the first image comprises isolating a feature of interest from an image and determining a feature of interest using an edge detection technique.

41. An apparatus for real time image generation, the apparatus comprising: at least an optical system; a slide port configured to hold a slide; an actuator mechanism mechanically connected to a mobile element; a user interface; at least a processor; and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to: using the at least an optical system, capture a first image of the slide at a first position; receive a first parameter set; 150 Attorney Docket No.1519-032PCT1using the actuator mechanism, move the mobile element into a second position, wherein the second position is determined based on the first parameter set; using the at least an optical system, capture a second image of the slide at the second position; and using the user interface, display the second image to a user.

42. The apparatus of claim 41, wherein the first image comprises an image of the entire slide.

43. The apparatus of claim 41, wherein the second image comprises a plurality of images of a region of interest of the slide at differing focus depths for each image of the plurality of images.

44. The apparatus of claim 41, wherein the memory contains further instructions configuring the at least a processor to add an annotation to the second image based as a function of an annotation instruction.

45. The apparatus of claim 41, wherein the memory contains further instructions configuring the at least a processor to identify at least an artifact within the first image using a machine vision system.

46. The apparatus of claim 45, wherein the memory contains further instructions configuring the at least a processor to remove the at least an artifact from the first image.

47. The apparatus of claim 41, wherein the memory contains further instructions configuring the at least a processor to identify a second parameter set as a function of the first parameter set, wherein the second parameter set comprises a level of zoom associated with a region of interest of the slide.

48. The apparatus of claim 41, wherein the memory contains further instructions configuring the at least a processor to modify the second image, wherein modifying the second image comprises replacing at least a region of the first image with the second image to create a hybrid image.

49. The apparatus of claim 41, wherein moving the mobile element into the second position comprises setting a focus height of the at least an optical system based on a focus height specified by the first parameter set.

50. The apparatus of claim 41, wherein moving the mobile element into the second position comprises positioning, using the actuator mechanism, the at least an optical system relative to the slide such that a region of interest of the slide defined by the first 151 Attorney Docket No.1519-032PCT1parameter set is within a frame of the at least an optical sensor of the at least an optical system.

51. A method for real time image generation, the method comprising: capturing, by at least a processor, a first image of a slide at a first position using at least an optical system; receiving, by the at least a processor, a first parameter set; moving, by the at least a processor, a mobile element into a second position, wherein the second position is determined based on the first parameter set using an actuator mechanism; capturing, by the at least a processor, a second image of the slide at the second position using at least an optical system; and displaying, by the at least a processor, the second image to a user using a user interface.

52. The method of claim 51, wherein the first image comprises an image of the entire slide.

53. The method of claim 51, wherein the second image comprises a plurality of images of a region of interest of the slide at differing focus depths for each image of the plurality of images.

54. The method of claim 51, wherein the method further comprises adding, by the at least a processor, an annotation to the second image based as a function of an annotation instruction.

55. The method of claim 51, wherein the method further comprises identifying, by the at least a processor, at least an artifact within the first image using a machine vision system.

56. The method of claim 55, wherein the method further comprises removing, by the at least a processor, the at least an artifact from the first image.

57. The method of claim 51, wherein the method further comprises identifying, by the at least a processor, a second parameter set as a function of the first parameter set, wherein the second parameter set comprises a level of zoom associated with a region of interest within the slide.

58. The method of claim 51, wherein the method further comprises modifying, by the at least a processor, the second image, wherein modifying the second image comprises replacing at least a region of the first image with the second image to create a hybrid image. 152 Attorney Docket No.1519-032PCT159. The method of claim 51, wherein moving the mobile element into the second position comprises setting a focus height of the at least an optical system based on a focus height specified by the first parameter set.

60. The method of claim 51, wherein moving the mobile element into the second position comprises positioning, using the actuator mechanism, the at least an optical system relative to the slide such that a region of interest of the slide defined by the first parameter set is within a frame of the at least an optical sensor of the at least an optical system.

61. An apparatus for real time image generation, the apparatus comprising: at least an optical system; a slide port configured to hold a slide; an actuator mechanism mechanically connected to a mobile element; a user interface; at least a processor; and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to: using the at least an optical system, capture a first image of the slide at a first position; receive a first parameter set; using the actuator mechanism, move the mobile element into a second position, wherein the second position is determined based on the parameter set; using the at least an optical system, capture a second image of the slide at the second position; using the at least an optical system, generate a hybrid image by replacing at least a region of the first image with the second image, wherein generating the hybrid image comprises: identifying at least an artifact within the hybrid image; and removing the at least an artifact from the hybrid image, wherein removing the at least an artifact comprises: 153 Attorney Docket No.1519-032PCT1receiving the first image by an artifact removal machine learning model, wherein the artifact removal machine learning model comprises a generative machine learning model; and removing the at least an artifact from the hybrid image using the artifact removal machine learning model; and using the user interface, display the hybrid image to a user.

62. The apparatus of claim 61, wherein removing the at least an artifact further comprises: generating synthetic training data using the artifact removal machine learning model, wherein the synthetic training data comprises a set of images containing artifacts correlated to a set of images without artifacts; and training the artifact removal machine learning model using the synthetic training data.

63. The apparatus of claim 61, wherein the first image comprises an image of the entire slide.

64. The apparatus of claim 61, wherein the second image comprises a plurality of images of the region of interest of the slide at differing focus depths for each image of the plurality of images.

65. The apparatus of claim 61, wherein the memory contains further instructions configuring the at least a processor to add an annotation to the second image based as a function of an annotation instruction.

66. The apparatus of claim 61, wherein the memory contains further instructions configuring the at least a processor to identify the at least an artifact within the first image using a machine vision system.

67. The apparatus of claim 61, wherein the memory contains further instructions configuring the at least a processor to identify a second parameter set as a function of the first parameter set, wherein the second parameter set comprises a level of zoom associated with the region of interest within the slide.

68. The apparatus of claim 61, wherein the memory contains further instructions configuring the at least a processor to identify a second parameter set as a function of the first parameter set, wherein the second parameter set comprises a focus depth associated with the region of interest within the slide. 154 Attorney Docket No.1519-032PCT169. The apparatus of claim 61, wherein moving the mobile element into the second position comprises setting a focus height of the at least an optical system based on a focus height specified by the first parameter set.

70. The apparatus of claim 61, wherein moving the mobile element into the second position comprises positioning, using the actuator mechanism, the at least an optical system relative to the slide such that the region of interest of the slide defined by the first parameter set is within a frame of the at least an optical sensor of the at least an optical system.

71. A method for real time image generation, the method comprising: capturing, using at least a processor and at least an optical system, a first image of a slide at a first position; receiving, by the at least a processor, a first parameter set; removing, by the at least a processor, at least an artifact from the first image, wherein removing the at least an artifact comprises: receiving the first image by an artifact removal machine learning model, wherein the artifact removal machine learning model comprises a generative machine learning model; and removing the at least an artifact from the first image using the artifact removal machine learning model; moving, by the at least a processor, a mobile element into a second position, wherein the second position is determined based on the first parameter set using an actuator mechanism; capturing, by the at least a processor, a second image of the slide at the second position using at least an optical system; generating, by the at least a processor, a hybrid image by replacing at least a region of interest of the first image with the second image to create a hybrid image using the at least an optical system, wherein generating the hybrid image comprises: identifying at least an artifact within the hybrid image; and removing the at least an artifact from the hybrid image, wherein removing the at least an artifact comprises: 155 Attorney Docket No.1519-032PCT1receiving the first image by an artifact removal machine learning model, wherein the artifact removal machine learning model comprises a generative machine learning model; and removing the at least an artifact from the hybrid image using the artifact removal machine learning model; and displaying, by the at least a processor, the hybrid image to a user using a user interface.

72. The method of claim 71, wherein removing the at least an artifact further comprises: generating synthetic training data using the artifact removal machine learning model, wherein the synthetic training data is comprised of images comprising artifacts correlated to images without artifacts; and training the artifact removal machine learning model using the synthetic training data.

73. The method of claim 71, wherein the first image comprises an image of the entire slide.

74. The method of claim 71, wherein the second image comprises a plurality of images of a region of interest of the slide at differing focus depths for each image of the plurality of images.

75. The method of claim 71, wherein the method further comprises adding, by the at least a processor, an annotation to the second image based as a function of an annotation instruction.

76. The method of claim 71, wherein the method further comprises identifying, by the at least a processor, the at least an artifact within the first image using a machine vision system.

77. The method of claim 71, wherein the method further comprises identifying, by the at least a processor, a second parameter set as a function of the first parameter set, wherein the second parameter set comprises a level of zoom associated with the region of interest within the slide.

78. The method of claim 71, wherein the method further comprises identifying, by the at least a processor, a second parameter set as a function of the first parameter set, wherein the second parameter set comprises a focus depth associated with the region of interest within the slide.

79. The method of claim 71, wherein moving the mobile element into the second position comprises setting a focus height of the at least an optical system based on a focus height specified by the first parameter set. 156 Attorney Docket No.1519-032PCT180. The method of claim 71, wherein moving the mobile element into the second position comprises positioning, using the actuator mechanism, the at least an optical system relative to the slide such that a region of interest of the slide defined by the first parameter set is within a frame of the at least an optical sensor of the at least an optical system. 157 Attorney Docket No.1519-032PCT1

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