Systems and methods for augmented visualization using activity windows

Abstract

Systems for augmented visualization using activity windows may include at least a processor, a memory communicatively connected to the at least a processor and an interactive display device. The memory may store instructions configuring the processor to initiate a method for image processing and labeling for user display. A method for augmented visualization using activity windows may include receiving image data from an imaging device, executing at least a first algorithm on the image data, wherein the first algorithm is configured to output annotation data associated with the image data, generating a display data structure, and displaying the display data structure at interactive display device.

Description

SYSTEMS AND METHODS FOR AUGMENTED VISUALIZATION USING ACTIVITY WINDOWSCROSS-REFERENCE TO RELATED APPLICATIONSThis application claims the benefit of priority of U.S. Nonprovisional Application Serial No. 18 / 660,007, filed on May 9, 2024, and entitled “SYSTEMS AND METHODS FOR AUGMENTED VISUALIZATION USING ACTIVITY WINDOWS,” which is incorporated by reference herein in its entirety.FIELD OF THE INVENTIONThe present invention generally relates to the field of image processing. In particular, the present invention is directed to systems and methods for augmented visualization using activity windows.BACKGROUNDVisualizing images at multiple resolutions with overlapping overlays may occlude image content. This may make the visualization process particularly difficult and cause certain content to be missed. For example, viewing biological slide images can be a challenging process. A user may be overwhelmed by the information presented on the user interface (UI) when overlays or other features are present. A UI solution is needed that allows a user to view all of the information generated by image processing algorithms without overwhelming the user or causing occlusion of certain image content.SUMMARY OF THE DISCLOSUREIn an aspect, a system for augmented visualization using activity windows includes at least a processor, a memory communicatively connected to the at least a processor, and an interactive display device. The memory contains instructions configuring the at least a processor to receive image data from an imaging device, execute at least a first algorithm on the image data, which is configured to output annotation data associated with image data, and generate a display data structure. The interactive display device may be configured to display to a user the generated display data structure. The display data structure includes a primary window and an activity window, wherein the activity window includes overlays, metadata, and / or the like.In an embodiment, the imaging device may include an optical scanner. In an embodiment, the at least a first algorithm may include on or more of the following algorithms: an i Atorney Docket No. 1519-174PCT1algorithm configured to calculate a fitness measure of the image data and flag the image data accordingly, an algorithm configured to determine a quality metric for the image data, an algorithm configured to identify different cell groups in one or more image data, and an algorithm configured to generate a color gamut correction for one or more image data. In an embodiment, the activity window may further include adaptive overlays with metadata at different levels of magnification. In an embodiment, the adaptive overlays may further include transparent masks with overlay information at high magnification, contours with overlay information at intermediate magnification, and dots of various sizes with information at lower magnification. In an embodiment, a user may toggle between annotation data displayed on the activity window at the interactive display device. In an embodiment, the instructions may further configure the processor to accept user input, using interactive display device, selecting a region of interest of image data, display, using the primary window, the selected region of interest, and enable, using interactive display device, zoom and pan over displayed region of interest. In an embodiment, the instructions may further configure the processor to accept user input, using image segmentation tools, multiple segments of interest from display data structure, composite a virtual composite image from the selected segments of interest, and display the virtual composite image. In an embodiment, the activity window and the primary window may present adjacent image data, wherein the adjacent image data includes scanned tissue slides with different stains. In an embodiment, the adjacent image data may further include scanned tissue slides, and the altered image data may further include scanned tissue slides with different stains.In another aspect, a method for augmented visualization using activity windows includes receiving image data from an imaging device, executing at least a first algorithm on the image data, generating a display data structure, and displaying the display data structure at the interactive display device. This method and other embodiments as described below may be implemented on any embodiment of system for augmented visualization using activity windows as described throughout this disclosure.In an embodiment, the imaging device may include an optical scanner. In an embodiment, the at least a first algorithm may include on or more of the following algorithms: an algorithm configured to calculate a fitness measure of the image data and flag the image data accordingly, an algorithm configured to determine a quality metric for the image data, an algorithm configured to identify different cell groups in one or more image data, and an2 Atorney Docket No. 1519-174PCT1algorithm configured to generate a color gamut correction for one or more image data. In an embodiment, the activity window may further include adaptive overlays with metadata at different levels of magnification. In an embodiment, the adaptive overlays may further include transparent masks with overlay information at high magnification, contours with overlay information at intermediate magnification, and dots of various sizes with information at lower magnification. In an embodiment, a user may toggle between annotation data displayed on the activity window at the interactive display device. In an embodiment, the method may further include accepting user input, using interactive display device, selecting a region of interest of image data, displaying, using the primary window, the selected region of interest, and enabling, using interactive display device, zoom and pan over displayed region of interest. In an embodiment, the method may further include accepting user input, using image segmentation tools, multiple segments of interest from display data structure, compositing a virtual composite image from the selected segments of interest, and displaying the virtual composite image. In an embodiment, the activity window and the primary window may present adjacent image data, wherein the adjacent image data includes scanned tissue slides with different stains. In an embodiment, the adjacent image data may further include scanned tissue slides, and the altered image data may further include scanned tissue slides with different stains.In another aspect, a system for augmented visualization using activity windows includes at least a processor, a memory communicatively connected to the at least a processor, and an interactive display device. The memory contains instructions configuring the at least a processor to receive image data from an imaging device, execute at least a first algorithm on the image data comprising an algorithm configured to determine a quality metric from the image data by implementing a process to correct image data using historical image data and to identify different cell groups in the image data, which is configured to output annotation data associated with image data, and generate a display data structure, wherein the display data structure includes at least a primary window and an activity window to visualize the image data with overlay of metadata including annotation data. The interactive display device is configured to display to a user the generated display data structure.In an embodiment, the imaging device may include an optical scanner. In an embodiment, the at least a first algorithm may include on or more of the following algorithms: an algorithm configured to calculate a fitness measure of the image data and flag the image data3 Atorney Docket No. 1519-174PCT1accordingly, an algorithm configured to determine a quality metric for the image data, an algorithm configured to identify different cell groups in one or more image data, and an algorithm configured to generate a color gamut correction for one or more image data. In an embodiment, the activity window may further include adaptive overlays with metadata at different levels of magnification. In an embodiment, the adaptive overlays may further include transparent masks with overlay information at high magnification, contours with overlay information at intermediate magnification, and dots of various sizes with information at lower magnification. In an embodiment, a user may toggle between annotation data displayed on the activity window at the interactive display device. In an embodiment, the instructions may further configure the processor to accept user input, using interactive display device, selecting a region of interest of image data, display, using the primary window, the selected region of interest, and enable, using interactive display device, zoom and pan over displayed region of interest. In an embodiment, the instructions may further configure the processor to accept user input, using image segmentation tools, multiple segments of interest from display data structure, composite a virtual composite image from the selected segments of interest, and display the virtual composite image. In an embodiment, the activity window and the primary window may present adjacent image data, wherein the adjacent image data includes scanned tissue slides with different stains. In an embodiment, the adjacent image data may further include scanned tissue slides, and the altered image data may further include scanned tissue slides with different stains.In another aspect, a method for augmented visualization using activity windows includes receiving image data from an imaging device, executing at least a first algorithm on the image data comprising an algorithm configured to determine a quality metric from the image data by implemented a process to correct image data using historical image data and to identify different cell groups in the image data, wherein the first algorithm is configured to output annotation data associated with the image data, generating a display data structure, wherein the display data structure includes at least a primary window and an activity window to visualize the image data with overlay of metadata including annotation data, and displaying the display data structure at the interactive display device. This method and other embodiments as described below may be implemented on any embodiment of system for augmented visualization using activity windows as described throughout this disclosure.In an embodiment, the imaging device may include an optical scanner. In an4 Atorney Docket No. 1519-174PCT1embodiment, the at least a first algorithm may include on or more of the following algorithms: an algorithm configured to calculate a fitness measure of the image data and flag the image data accordingly, an algorithm configured to determine a quality metric for the image data, an algorithm configured to identify different cell groups in one or more image data, and an algorithm configured to generate a color gamut correction for one or more image data. In an embodiment, the activity window may further include adaptive overlays with metadata at different levels of magnification. In an embodiment, the adaptive overlays may further include transparent masks with overlay information at high magnification, contours with overlay information at intermediate magnification, and dots of various sizes with information at lower magnification. In an embodiment, a user may toggle between annotation data displayed on the activity window at the interactive display device. In an embodiment, the method may further include accepting user input, using interactive display device, selecting a region of interest of image data, displaying, using the primary window, the selected region of interest, and enabling, using interactive display device, zoom and pan over displayed region of interest. In an embodiment, the method may further include accepting user input, using image segmentation tools, multiple segments of interest from display data structure, compositing a virtual composite image from the selected segments of interest, and displaying the virtual composite image. In an embodiment, the activity window and the primary window may present adjacent image data, wherein the adjacent image data includes scanned tissue slides with different stains. In an embodiment, the adjacent image data may further include scanned tissue slides, and the altered image data may further include scanned tissue slides with different stains.In another aspect, a system for augmented visualization using activity windows includes at least a processor, a memory communicatively connected to the at least a processor, and an interactive display device. The memory contains instructions configuring the at least a processor to receive image data from an imaging device, execute one or more algorithms from a suite of algorithms, wherein a first algorithm includes an algorithm configured to calculate a fitness measure of the image data, wherein calculating a fitness measure of the image data includes determining a confidence score associated with a scanned label as a function of a comparison between the scanned label and a plurality of historical scanned labels, flagging the image data as a function of the confidence score, and capturing additional image data as a function of flagging the image data, and a second algorithm includes an algorithm configured to output annotation5 Atorney Docket No. 1519-174PCT1data associated with the image data, and generate a display data structure, wherein the display data structure includes at least a primary window and an activity window configured to visualize the image data with an overlay of metadata, wherein the metadata includes annotation data. The interactive display device is configured to display to a user, the display data structure.In an embodiment, determining a confidence score may include generating the confidence score using a confidence machine-learning model, wherein the confidence machinelearning model may be trained using a confidence training data set. In an embodiment, capturing additional image data as a function of flagging the image data may further include capturing, at the imaging device, in rapid succession a series of images of a subject matter at varying camera lens focal lengths and exposure times, and identifying, by the at least a processor, an image of the series of images of a subject matter with a highest focus quality for a selected region of interest. In an embodiment, the second algorithm may include one or more of an algorithm configured to determine a quality metric for the image data, an algorithm configured to identify different cell groups in one or more image data, and an algorithm configured to generate a color gamut correction for one or more image data. In an embodiment, the at least a processor may be further configured to segment the image data using a segmentation tool, display the segmented image data at the activity window, receive a user input, wherein the user input may include a selection of one or more segments of the segmented image data, and display the selection of one or more segments of the segmented image data at the primary window. In an embodiment, the activity window may include a first activity window, wherein the first activity window includes a first segmented image datum, and a second activity window, wherein the second activity window includes a second segmented image datum, and the at least a processor is further configured to receive a user input, wherein the user input includes a selection of one or more segments of the first segmented image datum and a selection of one or more of the segments of the second segmented image datum, and display at the primary window, the selection of one or more segments of the first segmented image datum and the selection of the one or more segments of the second segmented image datum. In an embodiment, the at least a processor may be further configured to receive a user input, wherein the user input includes a selected region of interest, wherein the selected region of interest is highlighted as a function of a drag-and-drop operation using a mouse, wherein the user initiates the selection by clicking on a starting point within the activity window, and drags a cursor to an endpoint, thereby defining the region of interest, and6 Atorney Docket No. 1519-174PCT1display the region of interest at the primary window. In an embodiment, the at least a processor may be further configured to receive a user input, wherein the user input includes a selection of a portion of the image data within the activity window to perform an image processing operation as a function of the second algorithm, apply the second algorithm to the image data, and display an enhanced image at the primary window. In an embodiment, the at least a processor may enable a user, using the interactive display device to zoom and pan over the display data structure.In another aspect, a method for augmented visualization using activity windows includes receiving image data from an imaging device, executing one or more algorithms from a suite of algorithms, wherein a first algorithm includes an algorithm configured to calculate a fitness measure of the image data, wherein calculating a fitness measure of the image data includes determining a confidence score associated with a scanned label as a function of a comparison between the scanned label and a plurality of historical scanned labels, flagging the image data as a function of the confidence score, and capturing additional image data as a function of flagging the image data, and a second algorithm includes an algorithm configured to output annotation data associated with the image data, and generating a display data structure, wherein the display data structure includes at least a primary window and an activity window configured to visualize the image data with an overlay of metadata, wherein the metadata includes annotation data.In an embodiment, determining a confidence score may include generating the confidence score using a confidence machine-learning model, wherein the confidence machinelearning model may be trained using a confidence training data set. In an embodiment, capturing additional image data as a function of flagging the image data may further include capturing, at the imaging device, in rapid succession a series of images of a subject matter at varying camera lens focal lengths and exposure times, and identifying, by at least a processor, an image of the series of images of a subject matter with a highest focus quality for a selected region of interest. In an embodiment, the second algorithm may include one or more of an algorithm configured to determine a quality metric for the image data, an algorithm configured to identify different cell groups in one or more image data, and an algorithm configured to generate a color gamut correction for one or more image data. In an embodiment, the method may further include segmenting the image data using a segmentation tool, displaying the segmented image data at the activity window, receiving a user input, wherein the user input may include a selection of one or7 Atorney Docket No. 1519-174PCT1more segments of the segmented image data, and displaying the selection of one or more segments of the segmented image data at the primary window. In an embodiment, the activity window may include a first activity window, wherein the first activity window includes a first segmented image datum, and a second activity window, wherein the second activity window includes a second segmented image datum, and the method further includes receiving a user input, wherein the user input includes a selection of one or more segments of the first segmented image datum and a selection of one or more of the segments of the second segmented image datum, and displaying at the primary window, the selection of one or more segments of the first segmented image datum and the selection of the one or more segments of the second segmented image datum. In an embodiment, the method may include receiving a user input, wherein the user input includes a selected region of interest, wherein the selected region of interest is highlighted as a function of a drag-and-drop operation using a mouse, wherein the user initiates the selection by clicking on a starting point within the activity window, and drags a cursor to an endpoint, thereby defining the region of interest, and displaying the region of interest at the primary window. In an embodiment, the method may further include receiving a user input, wherein the user input includes a selection of a portion of the image data within the activity window to perform an image processing operation as a function of the second algorithm, applying the second algorithm to the image data, and displaying an enhanced image at the primary window. In an embodiment, the method may further include enabling a user, using the interactive display device to zoom and pan over the display data structure.These and other aspects and features of non-limiting embodiments of the present invention will become apparent to those skilled in the art upon review of the following description of specific non-limiting embodiments of the invention in conjunction with the accompanying drawings.DESCRIPTION OF THE DRAWINGSFor 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. l is a block diagram illustrating a system for augmented visualization using activity windows;FIG. 2 illustrates a particular implementation of a system for augmented visualization using8 Atorney Docket No. 1519-174PCT1activity windows, wherein a user may highlight a portion of the image data;FIG. 3 illustrates a particular implementation of a system for augmented visualization using activity windows, wherein a user may select a segment of image data to view;FIG. 4 illustrates a particular implementation of a system for augmented visualization using activity windows, wherein selected segments from the activity window are shown;FIG. 5 illustrates a particular implementation of a system for augmented visualization using activity windows, wherein selected segments from multiple activity windows are shown;FIG. 6 is a flow diagram of an exemplary embodiment of a data flow for inline quality control; FIGS. 7A-7B are diagrams of an exemplary embodiment of a localization module of a data flow for inline quality control;FIG. 8 is a diagram of an exemplary embodiment of a biopsy plane estimation module of a data flow for inline quality control;FIG. 9 is a diagram of an exemplary embodiment of a focus sampling module of a data flow for inline quality control;FIGS. 10A-10B are diagrams of an exemplary embodiment of a z-stack acquisition module of a data flow for inline quality control;FIGS. 11A-1 IB are diagrams of an exemplary embodiment of a stitching module of a data flow for inline quality control;FIG. 12 is a diagram depicting an exemplary embodiment of a system for digitizing a slide;FIG. 13 is a simplified diagram illustrating different example serial sections, in accordance with some embodiments;FIG. 14 is a simplified diagram illustrating serial sections on different slides at various stages during slide digitization, in accordance with some embodiments;FIG. 15 is a simplified diagram illustrating serial sections on the same slide at various stages during slide digitization, in accordance with some embodiments;FIG. 16 illustrates an exemplary embodiment of a simplified system for color gamut normalization for digital slides;FIG. 17 illustrates an exemplary embodiment image of the original object view at a magnification level;FIG. 18 illustrates an exemplary embodiment image of a processed image representation described in FIG. 17;9 Atorney Docket No. 1519-174PCT1FIG. 19 illustrates an exemplary embodiment image of a segmented image representation described in FIG. 17;FIG. 20 illustrates an exemplary embodiment image of transformed image representation described in FIG. 17;FIG. 21 illustrates an exemplary embodiment image of a region representation described in FIG. 17;FIG. 22 illustrates an exemplary machine-learning module;FIG. 23 illustrates an exemplary neural network;FIG. 24 illustrates an exemplary node of a neural network;FIG. 25 an illustration exemplary embodiment of fuzzy set comparison;FIG. 26 is a flow diagram illustrating an exemplary method for image processing and labeling for user display; andFIG. 27 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.DETAILED DESCRIPTIONAt a high level, aspects of the present disclosure are directed to systems and methods for image processing and labeling for user display. In an embodiment, systems for augmented visualization using activity windows may include at least a processor, a memory communicatively connected to the at least a processor and an interactive display device. The memory may store instructions configuring the processor to initiate a method for image processing and labeling for user display. A method for augmented visualization using activity windows may include receiving image data from an imaging device, executing at least a first algorithm on the image data, wherein the first algorithm is configured to output annotation data associated with the image data, generating a display data structure, and displaying, at interactive display device, the display data structure.Aspects of the present disclosure can be used to process and label images for user display. Aspects of the present disclosure can also be used to unclutter a user’s viewing io Atorney Docket No. 1519-174PCT1experience. This is so, at least in part, because the display data structure includes at least a primary window and an activity window. This allows a user to view image data with or without annotations or other overlays intended to aid a viewer.Aspects of the present disclosure allow for augmented visualization using activity windows. 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 system 100 for augmented visualization using activity windows is illustrated. Systems for augmented visualization using activity windows may include at least a processor 108, a memory 112 communicatively connected to the at least a processor 108 and an interactive display device 160. Memory 112 may store instructions 116 configuring processor 108 to receive image data 120 from imaging device 124, execute at least a first algorithm 140 on image data 120, and generate display data structure 148. Interactive display device 160 may be configured to display an embodiment of display data structure 148 in accordance with user input 164.With further reference to FIG. 1, system 100 may be configured to receive image data 120 from imaging device 124. Image data 120 may include one or more image files. For example, and without limitation image data 120 may include one or more raster image files and / or one or more vector image files. This may further include image extensions such as Joint Photographic Experts Group (JPEG), Portable Network Graphics (PNG), Graphics Interchange Format (GIF), Tagged Image File (TIFF), Photoshop Document (PSD), Portable Document Format (PDF), Encapsulated Postscript (EPS), Adobe Illustrator Document (Al), Adobe InDesign Document (INDD), and / or Raw Image Formats (RAW). As a nonlimiting example, image data 120 may include one or more scans of one or more tissue slides. As used throughout this disclosure, “tissue slides,” refers to a slide that exhibits a biological specimen for viewing. A tissue slide may include a plurality of different cell types, a single cell type, and / or the like. Image data 120 may be received from imaging device 124 directly and / or from storage device 128 where image data 120 may be stored. Imaging device 124 may additionally be communicatively connected to storage device 128. Imaging device 124 is any device that is designed and / or configured to capture a digitized visual of a real-life element. Imaging device 124 may include an optical scanner, x-rays, computed tomography (CT) scanners, ultrasonography, mammography, positron-emission tomography (PET), and / or the like. In some i i Atorney Docket No. 1519-174PCT1embodiments, imaging device 124 may include a table on which a tissue slide may be mounted. Further, in some embodiments the table may be moveable in the X, Y, Z directions.Continuing to reference FIG. 1, in some embodiments, imaging device 124 additionally include at least a camera. As used in this disclosure, a “camera” is a device that is configured to sense electromagnetic radiation, such as without limitation visible light, and generate an image representing the electromagnetic radiation. 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 cases, 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- oxi de- semi conductor (CMOS) sensors, chemical image sensors, and analog image sensors, such as without limitation film. In some cases, a camera may be sensitive within a non-visible range of electromagnetic radiation, such as without limitation infrared. As used in this disclosure, “image data” is information representing at least a physical scene, space, and / or object. In some cases, image data 120 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 material, such as without limitation when film is used to capture an image. 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.Still referring to FIG. 1, in some embodiments, system 100 may include a machine vision system that includes at least a camera. A machine vision system may use 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 / or the like. Non-limiting example of feature detection may include scale invariant feature transform (SIFT), Canny edge detection, Shi Tomasi corner detection, and / or 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 limitation12 Atorney Docket No. 1519-174PCT1homography 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 stereocamera), 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 < / > 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.With continued reference to FIG. 1, system 100 may be configured to execute at least a first algorithm 140 on image data 120. In an embodiment, machine learning module 132 may be13 Atorney Docket No. 1519-174PCT1configured to execute one or more of the algorithms discussed below. Training of machine learning module 132 may take place at computing device 104 and / or remotely. Exemplary training data 136 may vary depending on the algorithm. Retraining of machine learning module 132 may take place at computing device 104 and / or remotely. Additionally, outputs of machine learning module 132 may reiteratively be used as new training data 136. At least a first algorithm 140 may be configured to output annotation data 144 associated with image data 120. In an embodiment, at least a first algorithm 140 may include one or more of the following algorithms: an algorithm configured to calculate a fitness measure of the image data 120 and flag the image data 120 accordingly, an algorithm configured to determine a quality metric from the image data 120, an algorithm configured to identify different cell groups in one or more image data 120, and / or an algorithm configured to generate a color gamut correction for one or more image data 120. At least a first algorithm 140 may include any algorithm as described specifically herein and / or any other algorithm constructed to aid in image processing and has an influence on a viewer’s display of an image. Implementation of one or more of at least a first algorithm 140 may be assisted by a machine vision system as described above and / or any other imaging device as described throughout this disclosure.With continued reference to FIG. 1, in an embodiment, and without limitation, machine learning module 132 may comprise a deep neural network (DNN). As used in this disclosure, a “deep neural network” is defined as a neural network with two or more hidden layers. Neural network is described in further detail below with reference to FIGS. 7 -8. In a non-limiting example, machine learning module 132 may include a convolutional neural network (CNN). Generating exemplary outputs may include training CNN using the exemplary, nonlimiting training data listed for each individual algorithm. A “convolutional neural network,” for the purpose of 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. In some cases, CNN may include, without limitation, a deep neural network (DNN) extension. Mathematical (or convolution) operations performed in the convolutional layer may include convolution of two or more functions, where the kernel may be applied to input data e.g., any of the exemplary training data as described throughout this disclosure through a sliding window approach. In some cases, convolution operations may enable processor 108 to detect14 Atorney Docket No. 1519-174PCT1local / global patterns, edges, textures, and any other features described herein within. Spatial features may be passed through one or more activation functions, such as without limitation, Rectified Linear Unit (ReLU), to introduce non-linearities into the processing step of generating exemplary outputs. Additionally, or alternatively, CNN may also include one or more pooling layers, wherein each pooling layer is configured to reduce the dimensionality of input data while preserving essential features within the input data. In a non-limiting example, CNN may include one or more pooling layer configured to reduce the dimensions of spatial feature maps by applying downsampling, such as max-pooling or average pooling, to small, non-overlapping regions of one or more features.Still referring to FIG. 1, CNN may further include one or more fully connected layers configured to combine features extracted by the convolutional and pooling layers as described above. In some cases, one or more fully connected layers may allow for higher-level pattern recognition. In a non-limiting example, one or more fully connected layers may connect every neuron (i.e., node) in its input to every neuron in its output, functioning as a traditional feedforward neural network layer. In some cases, one or more fully connected layers may be used at the end of CNN to perform high-level reasoning and produce the final output such as, without limitation, the exemplary outputs as described below in the context of each specific example. Further, each fully connected layer may be followed by one or more dropout layers configured to prevent overfitting, and one or more normalization layers to stabilize the learning process described herein.With continued reference to FIG. 1, in an embodiment, and without limitation a feature learning algorithm may be utilized to group divided image data 120 into categories. The grouped image data 120 may then be run through an image classifier, such as without limitation, a CNN. In some embodiments the CNN may label each section as a section to be displayed within display data structure 148 at interactive display device 160 and / or alternatively be used to create a more uniform grouping of categories. For example, and without limitation this may be used in color correction as described in further detail below. Additionally, this classification may be used to assist in any labeling process as described below in the context of an embodiment of at least a first algorithm 140. A “feature learning algorithm,” as used herein, is a machine-learning algorithm that identifies associations between elements of data in a data set, which may include without limitation a training data set, where particular outputs and / or inputs are not specified. For15 Atorney Docket No. 1519-174PCT1instance, and without limitation, a feature learning algorithm may detect co-occurrences of elements of data, as defined above, with each other. As a non-limiting example, feature learning algorithm may detect co-occurrences of elements, as defined above, with each other. Computing device may perform a feature learning algorithm by dividing elements or sets of data into various sub-combinations of such data to create new elements of data and evaluate which elements of data tend to co-occur with which other elements. In an embodiment, first feature learning algorithm may perform clustering of data. Clustering of data may be categorized based on the algorithm being implemented. For example, and without limitation, clusters may be categorized based on cell type, which may require a comparison of labeled data of specific cell types and unlabeled data of specific cell types.Continuing refer to FIG. 1, a feature learning and / or clustering algorithm may be implemented, as a non-limiting example, using a k-means clustering algorithm. A “k-means clustering algorithm” as used in this disclosure, includes cluster analysis that partitions n observations or unclassified cluster data entries into k clusters in which each observation or unclassified cluster data entry belongs to the cluster with the nearest mean. “Cluster analysis” as used in this disclosure, includes grouping a set of observations or data entries in way that observations or data entries in the same group or cluster are more similar to each other than to those in other groups or clusters. Cluster analysis may be performed by various cluster models that include connectivity models such as hierarchical clustering, centroid models such as k- means, distribution models such as multivariate normal distribution, density models such as density -based spatial clustering of applications with nose (DBSCAN) and ordering points to identify the clustering structure (OPTICS), subspace models such as biclustering, group models, graph-based models such as a clique, signed graph models, neural models, and the like. Cluster analysis may include hard clustering whereby each observation or unclassified cluster data entry belongs to a cluster or not. Cluster analysis may include soft clustering or fuzzy clustering whereby each observation or unclassified cluster data entry belongs to each cluster to a certain degree such as for example a likelihood of belonging to a cluster; for instance, and without limitation, a fuzzy clustering algorithm may be used to identify clustering of elements of a first type or category with elements of a second type or category, and vice versa. Cluster analysis may include strict partitioning clustering whereby each observation or unclassified cluster data entry belongs to exactly one cluster. Cluster analysis may include strict partitioning clustering with16 Atorney Docket No. 1519-174PCT1outliers whereby observations or unclassified cluster data entries may belong to no cluster and may be considered outliers. Cluster analysis may include overlapping clustering whereby observations or unclassified cluster data entries may belong to more than one cluster. Cluster analysis may include hierarchical clustering whereby observations or unclassified cluster data entries that belong to a child cluster also belong to a parent cluster.With continued reference to FIG. 1, computing device may generate a k-means clustering algorithm receiving unclassified data and outputs a definite number of classified data entry clusters wherein the data entry clusters each contain cluster data entries. K-means algorithm may select a specific number of groups or clusters to output, identified by a variableGenerating a k-means clustering algorithm includes assigning inputs containing unclassified data to a “k- group ” or “k-cluster ” based on feature similarity. Centroids of k-groups or k-clusters may be utilized to generate classified data entry cluster. K-means clustering algorithm may select and / or be provided variable by calculating k-means clustering algorithm for a range of k values and comparing results. K-means clustering algorithm may compare results across different values of k as the mean distance between cluster data entries and cluster centroid. K-means clustering algorithm may calculate mean distance to a centroid as a function of k value, and the location of where the rate of decrease starts to sharply shift, this may be utilized to select a k value. Centroids of k-groups or k-cluster include a collection of feature values which are utilized to classify data entry clusters containing cluster data entries. K-means clustering algorithm may act to identify clusters of closely related data, which may be provided with user cohort labels; this may, for instance, generate an initial set of user cohort labels from an initial set of data, and may also, upon subsequent iterations, identify new clusters to be provided new labels, to which additional data may be classified, or to which previously used data may be reclassified.With continued reference to FIG. 1, generating a k-means clustering algorithm may include generating initial estimates for k centroids which may be randomly generated or randomly selected from unclassified data input. K centroids may be utilized to define one or more clusters. K-means clustering algorithm may assign unclassified data to one or more k- centroids based on the squared Euclidean distance by first performing a data assigned step of unclassified data. K-means clustering algorithm may assign unclassified data to its nearest centroid based on the collection of centroids Ci of centroids in set C. Unclassified data may be assigned to a cluster based on argminci 3 cdist ci, x)2, where argmin includes argument of the17 Atorney Docket No. 1519-174PCT1minimum, ci includes a collection of centroids in a set C, and dist includes standard Euclidean distance. K-means clustering module may then recompute centroids by taking mean of all cluster data entries assigned to a centroid’s cluster. This may be calculated based on ci =3 SiXl. K-means clustering algorithm may continue to repeat these calculations until a stopping criterion has been satisfied such as when cluster data entries do not change clusters, the sum of the distances have been minimized, and / or some maximum number of iterations has been reached.Still referring to FIG. 1, k-means clustering algorithm may be configured to calculate a degree of similarity index value. A “degree of similarity index value” as used in this disclosure, includes a distance measurement indicating a measurement between each data entry cluster generated by k-means clustering algorithm and a selected element. Degree of similarity index value may indicate how close a particular combination of elements is to being classified by k- means algorithm to a particular cluster. K-means clustering algorithm may evaluate the distances of the combination of elements to the k-number of clusters output by k-means clustering algorithm. Short distances between an element of data and a cluster may indicate a higher degree of similarity between the element of data and a particular cluster. Longer distances between an element and a cluster may indicate a lower degree of similarity between a elements to be compared and / or clustered and a particular cluster.With continued reference to FIG. 1, k-means clustering algorithm selects a classified data entry cluster as a function of the degree of similarity index value. In an embodiment, k-means clustering algorithm may select a classified data entry cluster with the smallest degree of similarity index value indicating a high degree of similarity between an element and the data entry cluster. Alternatively or additionally k-means clustering algorithm may select a plurality of clusters having low degree of similarity index values to elements to be compared and / or clustered thereto, indicative of greater degrees of similarity. Degree of similarity index values may be compared to a threshold number indicating a minimal degree of relatedness suitable for inclusion of a set of element data in a cluster, where degree of similarity indices a-n falling under the threshold number may be included as indicative of high degrees of relatedness. The abovedescribed illustration of feature learning using k-means clustering is included for illustrative purposes only and should not be construed as limiting potential implementation of feature learning algorithms; persons skilled in the art, upon reviewing the entirety of this disclosure, will18 Atorney Docket No. 1519-174PCT1be aware of various additional or alternative feature learning approaches that may be used consistently with this disclosure.Further referencing FIG. 1, at least a first algorithm 140 may include an algorithm configured to calculate a fitness measure of the image data 120 and flag the image data 120 accordingly. As used throughout this disclosure, “fitness measure” is a benchmark for meeting certain standards and / or criteria. In an embodiment an algorithm configured to calculate a fitness measure of the image data 120 and flag the image data 120 accordingly may work in tandem with an algorithm configured to determine a quality metric from the image data 120.In continued reference to FIG. 1, an algorithm configured to calculate a fitness measure of image data 120 and flag image data 120 may instruct at least a processor 108 to receive a profile comprising at least a label containing a plurality of metadata associated with the at least a label, generate a scanned label as a function of the at least a label, wherein generating a scanned label comprises scanning the at least a label using a text recognition module, determine a confidence score associated with the scanned label as a function of a comparison between the scanned label and a plurality of historical scanned labels, and display the confidence score using a display device 160.With continued reference to FIG 1, processor 108 may be configured to receive a profile from a user, this may include a user input 164. 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 may be made up of a plurality of slide data. As used in the current disclosure, “data” is information associated with slide. The profile 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 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 may be created by a processor 108, a user, medical professional, or a third party (e.g. Spouse, Support Staff, Family Member, and the like). The profile may include any of the following personal information: age, weight, height, gender, geographical location, diagnostic information, medical history, test result,19 Atorney Docket No. 1519-174PCT1lab result, and the like. A profile may include information medical information associated with patient and / or tissue.With continued reference to FIG 1, a profile contains a label 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 of histology slides. A label may contain a plurality of information regarding the histology slide. A label may contain information regarding a patient or case 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 may contain information regarding the type of biological sample, disease, tissue, morphology, and the like contained within the histology slide. This may include the type of tissue or sample that the slide represents, such as breast tissue, lung biopsy, skin lesion, cardiac tissue, liver tissue, and the like. In some cases, a label may contain information regarding a pathological diagnosis or condition being investigated or identified on the slide, such as carcinoma, lymphoma, or infectious disease. Labels 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 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 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 1, a label may come in a plurality of formats. The labels 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 for20 Atorney Docket No. 1519-174PCT1the 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 (ID) barcodes and two-dimensional (2D) barcodes. A linear or one-dimensional (ID) 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 ID 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 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. 1, a profile includes a plurality of metadata. As used in the current disclosure, “metadata” refers to descriptive information or attributes that provide context, structure, and meaning to data. Metadata is essentially data about data. Metadata 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 may include descriptive metadata, structural metadata, administrative metadata, technical metadata, provenance metadata, usage metadata, and the like. Metadata 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 standards include Dublin Core, Metadata Object Description Schema (MODS), and the Federal Geographic Data Committee (FGDC) metadata standard. In some cases, metadata may be associated with a label for a histology slide. Metadata may provide additional descriptive information or attributes that are linked to the slide's label. This metadata 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 associated with a label can vary based on the requirements and practices of the medical21 Atorney Docket No. 1519-174PCT1facility. Metadata 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 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 may include information related to the specific specimen type of the slide. This may include the type of tissue or sample that the slide represents. Metadata 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. 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. 1, processor 108 may be configured to generate a scanned label as a function of the at least a label. As used in the current disclosure, a “scanned label” is a label that has been converted from a physical document or an image to machine encoded text or binary code. A scanned label 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 allows for the automatic extraction and interpretation of the label's content for further processing or integration into a digital system. When a label is scanned, the scanning device captures the visual representation of the label, whether it is a barcode, text, or a combination of both. Processor 108 then then converts the scanned image into a scanned label that can be read and interpreted by a computer or software system. This includes converting the image associated with the label into machine encoded text. In a non-limiting example, if the label scanned using a barcode scanner, assuming the label is a barcode. The scanner converts the barcode into a scanned label which includes a binary code that22 Atorney Docket No. 1519-174PCT1represents the encoded data. In another non-limiting example, if the label 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 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 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 scanned label can be used for various purposes such as patient identification, case management, and slide categorization.With continued reference to FIG 1, processor 108 may be configured to generate a scanned label using a text recognition module. 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 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 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 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. 1, a text recognition module may include an optical character recognition (OCR) system. An optical character recognition system or optical character reader (OCR) may be configured to convert image data 120 of written text (e.g., typed, handwritten, or printed) into machine-encoded text. In some cases, recognition of at least a keyword from an image component may include one or more processes, including without limitation optical character recognition (OCR), optical word recognition, intelligent character recognition, intelligent word recognition, and the like. In some cases, OCR may recognize written text, one23 Atorney Docket No. 1519-174PCT1glyph 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 for 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 can 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 components. 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 the image component 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 the background of the image component. 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 the 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 a script allowing an appropriate OCR algorithm to be selected. In some24 Atorney Docket No. 1519-174PCT1cases, a character isolation or “segmentation” process may separate signal characters, for example, character-based OCR algorithms. In some cases, a normalization process may normalize the aspect ratio and / or scale of the image component.Still referring to FIG. 1, in some embodiments, an OCR process will 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 cases, 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 the image component. Matrix matching may also rely on a stored glyph being in a similar font and at the 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 features. 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 features can 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, machinelearning processes like nearest neighbor classifiers (e.g., k-nearest neighbors algorithm) can 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 machinelearning processes described with reference to FIG. 6-8. 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. The second pass may include adaptive recognition and use letter shapes recognized with high confidence on a first pass to recognize better remaining letters on the second pass. In some cases, a two-pass approach may be advantageous for unusual fonts or low-25 Atorney Docket No. 1519-174PCT1quality image components where visual verbal content may be distorted. Another exemplary OCR software tool includes OCRopus. OCRopus development is led by German Research Centre for Artificial Intelligence in Kaiserslautern, Germany.Still referring to FIG. 1, in some cases, OCR may include post-processing. For example, OCR accuracy can 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 visual verbal content. 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 use of a priori knowledge of grammar for a language to be 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.With continued reference to FIG 1, processor 108 may be configured to transform data extracted from a label into a digital format, wherein the digital format may be stored and manipulated electronically by the processor 108. 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.With continued reference to FIG. 1, processor 108 may be configured to generate a plurality of named entities as a function of the scanned label using a named entity recognition26 Atorney Docket No. 1519-174PCT1process. 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 semantics and identifying key elements of text is important.With continued reference to FIG 1, processor 108 may be configured to generate a plurality of named entities using a named entity recognition (NER) system. As used in the current disclosure, a “named entity recognition (NER) system” is software that identifies a plurality of named entities in from text. A NER system may be configured to identify a plurality of named entities from a scanned label. Inputs of a NER system may include a profile, label, scanned label, metadata, and the like. The output of a named entity recognition system may include a plurality of named entities. Named entities 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 1, a NER system may generate a plurality of named entities 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 and the scanned label, 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 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 layers27 Atorney Docket No. 1519-174PCT1of 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. Language processing models may include a program automatically generated by processor 108 and / or named entity recognition system to produce associations between one or more significant terms extracted from a scanned label and detect associations, including without limitation mathematical associations, between such significant terms. Associations between language elements, where language elements include for purposes 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 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 108, or the like.With continued reference to FIG. 1, processor 108 may classify a plurality of named entities 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 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,28 Atorney Docket No. 1519-174PCT1observations, notes, medical facility name, medical facility identification number, and the like. In an embodiment, a processor 108 may be configured to generate a plurality of entity categories based on the available the scanned label. Processor 108 may generate a plurality of entity categories based on historical versions of the entity categories. Processor 108 may generate a plurality of entity categories by extracting relevant features, characteristics, or traits associated with the scanned label. Identification of features may depend on the nature and type of histology slide. In a non-limiting example, plurality of named entities may be classified into a plurality of entity categories based on their semantic meaning. In other embodiments, a processor 108 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. 1, processor 108 may be configured to classify each named entity into the plurality of entity categories based on the named entities spatial position on the scanned label. As used in the current disclosure, “spatial position” refers to the positing of the named entity on the label. Spatial positioning may be a form of template base NER. Templatebased 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 108 is able to assign the content of each of those fields to an entity category. The spatial position on the label may be associated with one or more entity categories. The fields on a label 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 may be configured to have the same spatial positioning. In a non-limiting example, a label may include a spatial position or a field for a person’s name. To capture person names, processor 108 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 may be associated with an entity category. Thus, processor 108 may be configured to classify each named entity into entity categories based on the spatial position on a label. Processor 108 may be configured to identify spatial positions on the scanned label as a function of the medical facility that the scanned label is associated with.29 Atorney Docket No. 1519-174PCT1The medical facility may be identified using an alphanumeric code or another identifier on the label or within its associated metadata.With continued reference to FIG 1, processor 108 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. Data within the lookup table may be received from the database. Lookup tables may also be used to generate a plurality of named entities 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 includes 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 120 as an input and output a list of named entities. Processor 108 may be configured to “lookup” or input one or more scanned label, label, profile, metadata, 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 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. 1, processor 108 may be configured to generate a scanned label 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 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, label, metadata, a plurality of entity categories, examples of scanned labels, examples of named entities, and the like. Outputs to the label machine-learning model may include a scanned label and a listing of named entities. In some embodiments, a label machine learning model may be configured to sort30 Atorney Docket No. 1519-174PCT1a listing of named entities 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 correlated to examples of scanned labels. In another embodiment, label training data may comprise a plurality of labels correlated to examples of named entities. Label training data may be received from the database. Label training data may contain information regarding profile, label, metadata, a plurality of entity categories, examples of scanned labels, examples of named entities, 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 machinelearning 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 1, processor 108 determines a confidence score as a function of a comparison between the scanned label and a plurality of previously scanned labels. As used in the current disclosure, a “confidence score” is quantitative measurement of the accuracy of the content generated from scanned label. Accuracy of the content of the scanned label may refer to an accurate translation of the text of the label when generating the scanned label. Accuracy of the content of the scanned label may also refer to the likelihood that text of the scanned label accurately reflects the content of the histology slide. Processor 108 may generate a confidence score for each attribute of each entity. A confidence score may be used to normalize one or more scanned labels to bring all scanned labels 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 include min-max scaling, z-score normalization, or logarithmic transformation. In an embodiment, if content that is generated from the scanned label is accurate then the confidence score may be high, conversely if content that is generated from the scanned label is likely inaccurate then the confidence score may be low. A confidence score may be expressed as a numerical score, a linguistic value, alphanumeric score, or an alphabetical score. Confidence score may be represented as a score used to reflect the level of accuracy of the content of the scanned label. A non-limiting example, of a numerical score, may include a scale31 Atorney Docket No. 1519-174PCT1from 1-10, 1-100, 1-1000, and the like, wherein a rating of 1 may represent an inaccurate scanned label, whereas a rating of 10 may represent an accurate scanned label. In another nonlimiting 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 that receives a score between 50-75, on a scale from 1-100, may be considered “Moderately Accurate.”With continued reference to FIG. 1, a confidence score may be generated by comparing the current scanned label to historically scanned labels. As used in the current disclosure, “historically scanned labels” are scanned labels that have been generated prior to the current iteration of scanned labels. Processor 108 may identify a plurality of historically scanned labels as a function of the metadata associated with the label. The metadata may contain information regarding patient identifiers or medical facility identifiers. Based on the patient identifiers and / or medical facility identifiers, processor 108 may generate a plurality of historically scanned labels from the same facility and / or patient of the current scanned label. Processor 108 may compare the historically scanned labels the current scanned label may be comparing their content, spatial position, font, and the like. To compare a scanned label to historically scanned labels to verify the accuracy of the scanned label, a processor 108 may implement a process that involves data With continued reference to FIG. 1, in an embodiment, and without limitation a feature learning algorithm may be utilized to group divided image data 120 into categories. The grouped image data 120 may then be run through an image classifier, such as without limitation, a CNN. In some embodiments the CNN may label each section as a section to be displayed within data display structure matching and comparison. Processor 108 may extract relevant features or attributes are extracted from the current scanned label 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 108 may then apply a comparison algorithm to evaluate the similarity or differences between the extracted features of the current scanned label and the 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 108 may determine if there is a match or similar threshold that indicates the32 Atorney Docket No. 1519-174PCT1accuracy 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 108 may assign a confidence score to indicate the level of accuracy or similarity between the current scanned label 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. 1, a confidence score may include a derivation score. As used in the current disclosure, a “derivation score” is a score that addresses variations in scanned label 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 108 may be configured to preprocess the scanned label. Preprocessing may include steps to normalize the text attributes within the scanned label. This could involve standardizing the font, removing unnecessary formatting, or converting the text to a consistent style (e.g., removing italics or boldness). Processor 108 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 108 may then compare the OCR results of the scanned label 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 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. 1, a confidence score may include an expression score.33 Atorney Docket No. 1519-174PCT1As 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, or it may be aggregated to provide one score for all of the words included in the scanned label. Processor 108 may be configured to preprocess the scanned label. Preprocessing may include steps to normalize the text attributes within the scanned label. This could involve standardizing the font, removing unnecessary formatting, or converting the text to a consistent style (e.g., removing italics or boldness). Processor 108 may then extract a plurality of words from the scanned label. In some embodiments, an expression score may include a classification of each word of the scanned label to a named entity. This classification can be performed by mapping the expression scores to a predefined set of named entities or categories. For each extracted word, processor 108 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 the named entity of the plurality of named entities, 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. 1, a confidence score 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 above, a scanned label may include one or more barcodes. The scanned label (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 108 applies barcode recognition techniques to extract the barcode information from the scanned label. This process can involve decoding the barcode using specific algorithms or libraries designed for barcode recognition. Processor 108 then extracts any accompanying text or information present along with the barcode using the text recognition system, mentioned herein above. Processor 108 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, processor 108 may generate a consistency score that reflects the level of consistency between the barcode and the historically scanned label or other OCR generated text.34 Atorney Docket No. 1519-174PCT1If 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 (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. 1, a confidence score 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 108 may compare the handwritten document to other documents in the set to identify a discrepancy between the scanned label and historically scanned labels. The temporal consistency score may reflect the confidence processor 108 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 108 may place the scanned label through preprocessing steps to normalize and clean the data. This includes removing noise, correcting errors, and formatting the text for comparison. Processor 108 then compares the current scanned label 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, processor 108 may calculate a temporal consistency score that reflects the level of consistency or inconsistency between the current scanned label and the historically scanned labels. This score helps assess the temporal consistency and potential degradation of text recognition accuracy.With continued reference to FIG 1, Processor 108 may generate a confidence score as a function of the temporal consistency score, consistency score, expression score, and a derivation score. This embodiment of a confidence score may be an overall reflection of the confidence the content generated from scanned label. In an embodiment, a confidence score may be generated by averaging two or more of the above-mentioned scores. This may include assigning each score35 Atorney Docket No. 1519-174PCT1a weighted average. In another embodiment, a confidence score may be generated by evaluating the highest or lowest score among the four as the confidence score.With continued reference to FIG. 1, processor 108 may generate the confidence score using a score machine-learning model. As used in the current disclosure, an “score machinelearning model” is a machine-learning model that is configured to generate a confidence score. The score machine-learning model may be consistent with the machine-learning model described below in FIGS. 6-8 below. Inputs to the score machine-learning model may include profile, label, metadata, a plurality of entity categories, scanned labels, named entities, examples of confidence scores, and the like. Outputs to the score machine-learning model may include a confidence score tailored to one or more scanned labels. 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 correlated to examples of confidence scores. Examples of confidence scores may include historical confidence scores that have been generated from previous iterations of score machine learning model or system 100. Score training data may be received from the database. Score training data may contain information regarding profile, label, metadata, a plurality of entity categories, scanned labels, named entities, examples of confidence scores, and the like. In an embodiment, a score machine-learning model 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 and / or naive Bayes machine-learning models, nearest neighbor machinelearning 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. 1, processor 108 may generate the confidence score as a function of a comparison between the scanned label 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 and historically scanned labels.) and, based on a set of rules, assigns values to the output vector. A set of fuzzy rules may36 Atorney Docket No. 1519-174PCT1include 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 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 consequence determines the output or conclusion of the rule. In an embodiment, the confidence score 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. 1, confidence score may be determined as a function of the intersection between two fuzzy sets, wherein each fuzzy set may be representative of a scanned label and a historically scanned label respectively. Comparing the scanned label and 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 108 may use a fuzzy logic model to determine confidence score as a function of fuzzy set comparison techniques as described in this disclosure. In some embodiments, each piece of information associated with a scanned label may be compared to a historically scanned labels, wherein the confidence score 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 and a historically scanned labels to determine the confidence score specific to the scanned label. A fuzzy inferencing system may combine such linguistic variable values according to one or more fuzzy inferencing rules, including any type of37 Atorney Docket No. 1519-174PCT1fuzzy 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.In further reference to FIG. 1, when image data 120 is flagged, imaging device 124 may capture additional image data 120 and perform one or more of the algorithms described throughout this disclosure on the newly captured image data 120. This may include replacing the flagged image data 120 and / or integrating the image data 120 in accordance with one or more of the described methods in relation to at least a first algorithm 140.Continuing to reference FIG. 1, an algorithm configured to calculate a fitness measure of the image data 120 and flag the image data 120 accordingly may include training data 136 specific to such an embodiment of at least a first algorithm 140. Exemplary training data 136 may include, without limitation, inputs such as image data 120, flagged image data 120, fitness measurements, fitness measurement parameters, image data 120 parameters, rule-based actions correlated to fitness measurement parameters and image data 120 parameters, and / or the like correlated to outputs such as flagged image data 120, new image data 120 parameters, fitness measurements, and / or the like.With further reference to FIG. 1, at least a first algorithm 140 may include an algorithm configured to determine a quality metric from the image data 120. An algorithm configured to determine a quality metric from the image data 120 may configure processor 108 to perform a scan of the slide by capturing at least a first image and capturing at least a second image, wherein performing the scan includes identifying a first scanning parameter; using the optical system, capturing the at least a first image as a function of the first scanning parameter; determining a first quality metric as a function of the at least a first image; determining a second scanning parameter as a function of the first quality metric; and using the optical system, capturing the at least a second image as a function of the second scanning parameter.Still referring to FIG. 1, in some embodiments, system 100 may be used to generate an image of slide and / or a sample on slide. As used herein, a “slide” is a container or surface holding a sample of interest. In some embodiments, slide may include a glass slide. In some embodiments, slide may include a formalin fixed paraffin embedded slide. In some38 Atorney Docket No. 1519-174PCT1embodiments, a sample on slide may be stained. In some embodiments, slide may be substantially transparent. In some embodiments, slide may include a thin, flat, and substantially transparent glass slide. In some embodiments, a transparent cover may be applied to slide such that a sample is between slide and this cover. A sample may include, in non-limiting examples, a blood smear, pap smear, body fluids, and non-biologic samples. In some embodiments, a sample on slide may include tissue. In some embodiments, sample on slide may be frozen.Still referring to FIG. 1, in some embodiments, slide and / or a sample on slide may be illuminated. In some embodiments, system 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 and / or sample on slide. In a nonlimiting example, light source may illuminate slide and / or sample on slide from below.Still referring to FIG. 1, in some embodiments, system 100 may include a slide port. In some embodiments, slide port may be configured to hold slide. In some embodiments, slide port 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 secure, such as a clamp, latch, clip, recessed area, or another fastener. In some embodiments, slide port may allow for easy removal or insertion of slide. In some embodiments, slide port may include a transparent surface through which light may travel. In some embodiments, slide may rest on and / or may be illuminated by light traveling through such a transparent surface. In some embodiments, slide port may be mechanically connected to an actuator mechanism as described below. In some embodiments, a scanner may include a slide port.Still referring to FIG. 1, in some embodiments, system 100 may include an actuator mechanism. 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, the actuator mechanism may be mechanically connected to slide, such as slide in slide port. In some embodiments, actuator mechanism may be mechanically connected to slide port. For example,39 Atorney Docket No. 1519-174PCT1the actuator mechanism may move slide port in order to move slide. Tn some embodiments, actuator mechanism may be mechanically connected to at least an optical system. In some embodiments, actuator mechanism may be mechanically connected to a mobile element. A mobile element may include a movable or portable object, component, and / or device within system TOO 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 is positioned correctly with respect to slide such that optical system may capture an image of slide according to a scanning parameter. In some embodiments, actuator mechanism may be mechanically connected to an item selected from the list consisting of slide port, slide, and at least an optical system. In some embodiments, the actuator mechanism may be configured to change the relative position of slide and optical system by moving slide port, slide, and / or optical system. In some embodiments, a scanner may include an actuator mechanism.Still referring to FIG. 1, actuator mechanism may include a component of a machine that is responsible for moving and / or controlling a mechanism or system. Actuator mechanism 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 responds by converting source power into mechanical motion. In some cases, actuator mechanism may be understood as a form of automation or automatic control.Still referring to FIG. 1, in some embodiments, actuator mechanism 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 may include mechanical motion, such as without limitation linear, rotatory, or oscillatory motion. In some embodiments, hydraulic actuators 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 hydraulic40 Atorney Docket No. 1519-174PCT1cylinder 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 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, 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 may include an electric actuator. Electric actuator mechanism may include any of: electromechanical actuators, linear motors, and the like. In some cases, actuator mechanism 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 mechanisms 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 motors, U-channel linear motors and tubular linear motors. Linear motors may be directly41 Atorney Docket No. 1519-174PCT1controlled 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 may include a mechanical actuator mechanism. In some cases, a mechanical actuator mechanism 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 gears, rails, pulleys, cables, linkages, and the like.Still referring to FIG. 1, in some embodiments, the actuator mechanism 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 an actuator mechanism based on input received from a user interface. In some embodiments, actuator controls may be configured to operate actuator mechanism such that optical system is in a position to capture an image of an entire sample. In some embodiments, actuator controls may be configured to operate an actuator mechanism such that optical system is in a position to capture an image using settings of a particular scanning parameter. In some embodiments, actuator controls may be configured to operate an actuator mechanism such that optical system is in a position to capture an image of a region of interest, a particular horizontal row, a particular point, a particular focus depth, and the like. Electronic communication between actuator mechanism 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 signals may be received by actuator controls from processor or input interface.Still referring to FIG. 1, system 100 may perform inline quality control of slide digitization. In a non-limiting embodiment, system 100 may include one or more scanners that are communicatively coupled to processor 108. Scanners generally include devices or systems used to digitize slides containing biomedical specimens (e.g., tissue samples), such as digital cameras, digital microscopes, digital pathology scanners, or the like. In some embodiments, at least a processor 108 may be directly connected to and / or integrated within one or more of42 Atorney Docket No. 1519-174PCT1scanners. Additionally, or alternately, scanners may be communicatively coupled to processor 108 via a network. The network can include one or more local area networks (LANs), wide area networks (WANs), wired networks, wireless networks, the Internet, or the like. Illustratively, scanners may communicate with at least a processor 108 over network using the TCP / IP protocol or other suitable networking protocols. During operation, scanners capture digital images of the slides and send them to processor 108, e.g., via network. For efficient storage and / or transmission, images may be compressed prior to or during transmission. Security measures such as encryption, authentication (including multi-factor authentication), SSL, HTTPS, and other security techniques may also be applied.Still referring to FIG. 1, in some embodiments, at least a processor 108 may be configured to control the operation of scanners to automate the slide digitization process. For example, processor 108 may send instructions to scanners to scan or rescan selected portions of a slide (e.g., areas identified by x-, y-, and / or z-coordinates or bounding boxes). At least a processor 108 may further select parameters associated with scanners, such as magnification level, focus settings, scanning pattern, or the like.Still referring to FIG. 1, as discussed above, automating the slide digitization process using processor 108 may present challenges. For example, one or more images may be out of focus or may capture a different portion of the slide than intended, e.g., the actual coordinates of the image may be offset from the intended coordinates. In embodiments where a series of images of a given slide are captured (e.g., when the slide is scanned using a grid scanning pattern), the process of stitching the images together to generate an aggregated image of the slide may introduce artifacts, such as ghosting or banding. These defects may interfere with downstream applications that receive the digitized slide images output by processor 108. For example, the defects may result in a reduction in performance of a machine learning model trained using the digitized slides due to the reduced size and / or quality of the training data set. Moreover, the defects may be identified too late to efficiently take remedial action, e.g., a defective image may be removed from the data set (resulting in wasted resources that went into scanning the image), or the slide may be placed back into the scanner for another scanning pass (resulting in additional setup and takedown time). Such remedial actions may involve manual intervention, such that the level of automation in the slide digitization process is reduced.Still referring to FIG. 1, therefore, according to some embodiments, processor 10843 Atorney Docket No. 1519-174PCT1includes an inline quality control program, which may address one or more of the challenges identified above. When executed by processor 108, inline quality control program causes processor 108 to perform operations associated with inline quality control of slide digitization based on images. Illustrative embodiments of data flows implemented by inline quality control program are described in further detail below.Still referring to FIG. 1, during execution of inline quality control program, processor 108 may execute one or more neural network models. Neural network model is trained to make predictions (e.g., inferences) based on input data. Neural network model includes a configuration, which defines a plurality of layers of neural network model and the relationships among the layers. Illustrative examples of layers include input layers, output layers, convolutional layers, densely connected layers, merge layers, and the like. In some embodiments, neural network model may be configured as a deep neural network with at least one hidden layer between the input and output layers. Connections between layers can include feed-forward connections or recurrent connections.Still referring to FIG. 1, one or more layers of neural network model is associated with trained model parameters. The trained model parameters include a set of parameters (e.g., weight and bias parameters of artificial neurons) that are learned according to a machine learning process. During the machine learning process, labeled training data is provided as an input to neural network model, and the values of trained model parameters are iteratively adjusted until the predictions generated by neural network model match the corresponding labels with a desired level of accuracy.Still referring to FIG. 1, for improved performance, processor 108 may execute neural network model using a graphical processing unit, a tensor processing unit, an application-specific integrated circuit, or the like.With continued reference to FIG. 1, in an embodiment, an algorithm configured to determine a quality metric from image data 120 may implement blur detection and / or focus detection. Blur detection may be performed, as a non-limiting example, by taking Fourier transform, or an approximation such as a Fast Fourier Transform (FFT) of the image and analyzing a distribution of low and high frequencies in the resulting frequency-domain depiction of the image; numbers of high-frequency values below a threshold level may indicate blurriness. As a further non-limiting example, detection of blurriness may be performed by convolving an44 Atorney Docket No. 1519-174PCT1image, a channel of an image, or the like with a Laplacian kernel; this may generate a numerical score reflecting a number of rapid changes in intensity shown in the image, such that a high score indicates clarity, and a low score indicates blurriness. Blur detection may be performed using a Gradient-based operator, which measures operators based on the gradient or first derivative of an image, based on the hypothesis that rapid changes indicate sharp edges in the image, and thus are indicative of a lower degree of blurriness. 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. 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. Blur detection may be performed by using discrete cosine transform (DCT) coefficients in order to compute a focus level of an image from its frequency content. The quality of focus may be determined by analyzing a degree of focus at a portion of an image containing image data 120 of interest; this may be accomplished using any algorithm and / or operator as described above for blurriness detection and / or determination of degree of focus. Alternatively, or additionally, a whole-image blurriness detection process with regard to a section of image containing image data 120 of interest. Quality level may be determined according to a degree of lightness, darkness, contrast, and / or another parameter.In further reference to FIG. 1, in an embodiment, machine learning module 132 may also analyze a series of image data 120 taken in rapid succession of the same subject matter, but at varying camera lens focal lengths, exposure times, and / or the like. In this case the machine learning module 132 may identify the most in focus image corresponding to a selected region of the image.Further, an algorithm configured to determine a quality metric from image data 120 may additionally implement a process to correct image data 120 using other captured image data 120 of related matter. In an embodiment, a process to correct image data 120 may include the digitization of tissue slides based on associations among serial sections, wherein imaging device 124 is configured to scan a slide and send a digitized image of the slide to processor 108. In another aspect a method for digitizing tissue slides based on associations among serial sections may include receiving a candidate tissue map associated with a candidate tissue section, receiving a reference tissue map associated with a reference tissue section, aligning the candidate tissue map to the reference tissue map, comparing the aligned candidate tissue map to the45 Atorney Docket No. 1519-174PCT1reference tissue map, and generating a regenerated candidate tissue map as a function of the reference tissue map. In some embodiments, this algorithm may additionally generate scanning parameters to be used to recapture image data 120 in accordance with the quality level.Still referring to FIG. 1, imaging device 124 may be configured to capture a digital image, which may be referred to throughout this disclosure as digitized slide. Digitized slides may include any image produced using a digital camera and that may be stored as an electronic file. Some examples of this may include binary, grayscale, color, and / or multispectral images. Following digitization of the slide, imaging device 124 may send digital image as a digital file and / or data to processor 108 via communicative link between imaging device 124 and processor 108.Continuing to reference FIG. 1, in an embodiment slide digitization may include the retrieval of candidate slides and reference slides identified based on stain information of slides under assessment. In some embodiments, identification of candidate slides and / or reference slides may automatically be accomplished real-time alongside the digitization process by an inline computer program executed by processor 108. An “inline computing program,” refers to a computing term where code or data is inserted directly into its appropriate place within a larger block of code, rather than being called from a separate location. Alternatively, in some embodiments, identification of candidate slides and / or reference slides may be manually performed by a user. The identified candidate slide may be associated with information sufficient to identify one or more other slides from the same serial section set, which may be used as the reference slide. For example, and without limitation, candidate slide may have a corresponding case identification number and block identification number. Based on the case identification, as well as stain information of slides having the corresponding case identification number and the block identification number, another slide corresponding to the case identification number and the block identification number may be identified as a reference slide.Further referencing FIG. 1, in some embodiments identified reference slides may be an H&E-stained slide. In contrast, identified candidate slide may be a non-H&E-stained slide. For example, without limitation non-H&E-stained slides may be stained with Immunohistochemistry (IHC). IHC staining combines anatomical, immunological, and biochemical techniques to image discrete components in tissues by using appropriately labeled antibodies to bind specifically to their target antigens in situ. IHC staining makes it possible to visualize and document high-46 Atorney Docket No. 1519-174PCT1resolution distribution and localization of specific cellular components within cells and within their proper histological context. In some embodiments, identified reference slides may be a non- H&E-stained slide. When multiple candidate slides are identified, there may be any combination of a certain number of non-H&E-stained slides and a certain number of H&E-stained slides.Continuing to reference FIG. 1, processor 108 may be configured to retrieve image data of reference slide and image data 120 of candidate slide from scanned slides data repository to memory 112. The described retrieval operation by processor 108 may be based on the case identification number and the block identification number of candidate slide, which have been supplied to processor 108. Using image data 120 of reference slide and image data 120 of candidate slide, processor 108 generates reference tissue map file and candidate tissue map file, respectively. Reference tissue map file may contain data of the tissue map of candidate slide.With further reference to FIG. 1, in some embodiments, a user may identify a reference serial section and at least one non-reference serial action, otherwise described as a candidate serial section, on the same slide based on information such as shape and / or other attributes of the serial sections. The slide is also referred to as intra-serial sections slide. In turn, reference tissue map file may be generated by processor 108 from image data of reference serial section and candidate tissue map file may be generated by processor 108 from image data of candidate serial section.Continuing to reference FIG. 1, processor 108 may additionally be configured to align candidate tissue map file with reference tissue map file. During this alignment process, candidate tissue map files may be registered by processor 108 with the reference map file by leveraging information such as similarities of shape and size between mapped serial sections. For example, and without limitation, processor 108 may execute a registration module containing instructions stored in memory 112 to process candidate tissue map and reference tissue map. Processor 108 orients candidate tissue map, which may be stored in candidate tissue map file, to maximize the overlap between regions of interest identified in candidate tissue map and reference tissue map, which may be stored in reference tissue map file. Features used to align tissue maps may include tissue boundaries, contours, and / or other features that are visible in a slide, even in cases where such features may appear faint. The registration module may align one or more features that are common between candidate tissue map and reference tissue map. This may be accomplished by maximizing the overlap between common features in two or three dimensions in both tissue47 Atorney Docket No. 1519-174PCT1maps. Common features may include features that are detectable, such as, without limitation, features discernable using automated computer vision techniques. Maximizing overlap may be achieved by applying one or more matrix transformations, such as, without limitation, rotating, translating, scaling and / or skewing, to candidate tissue map and / or reference tissue map.With further reference to FIG. 1, processor 108 may be further configured to compare candidate tissue map file with reference tissue map file regarding properties such as shapes and sizes between mapped serial sections. In an embodiment, when a difference in compared properties is located as result of the comparison, differences are then utilized as correction factors to generate regenerated candidate tissue maps. In some embodiments, this process may include an initial step of generating a reference binary mask from reference tissue map and a candidate binary mask from candidate tissue map. These binary masks may identify the presence or absence of tissue across the slide in contrast to tissue maps which may provide a more detailed representation of the features on the slide. For example, and without limitation, binary masks may appear in a black and white format in contrast to RGB color format of features contained on a given slide. Reference binary mask and candidate binary mask may be aligned based on results of the alignment process as described above. For example, and without limitation, by applying matrix transformations determined by the registration module. Once aligned, adjustments may be made to candidate binary mask based on reference binary mask, resulting in a corrected candidate binary mask. As a nonlimiting example of this, a region of candidate binary mask originally identified as having no tissue present may be identified as having tissue in corrected candidate binary mask when the reference binary mask indicates that tissue is present in the region. Similarly, regions of candidate binary mask originally identified as having tissue present may be identified as having no tissue in corrected candidate binary mask when reference binary mask indicates that no tissue is present in the region. The corrected candidate binary mask may then be applied onto candidate tissue map, and candidate tissue map may be regenerated accordingly with corrections. Consequently, in some embodiments, the area of regenerated candidate tissue map where tissue is determined to be present is substantially equal to the corresponding area of reference tissue map.Continuing to reference FIG. 1, these and other factors may be streamlined by instantiation of a machine learning module and / or a neural network. Training data that may be used to train machine-learning model and / or neural network may include exemplary input data,48 Atorney Docket No. 1519-174PCT1such as without limitation, digitized slide data, such as without limitation class identification numbers, and / or block identification numbers, and / or the like, candidate tissue map data, reference tissue map data, regenerated candidate tissue map data, binary maps of any tissue map data, and / or the like, where each such example may be correlated to additional exemplary output data such as, without limitation, regenerated candidate tissue map data, and / or the like. Training of the model and / or network may take place either at processor 108 and / or remotely. In the latter case, the model and / or network may be deployed at or by processor 108 in any manner as described in this disclosure. In some embodiments, the machine-learning model and / or neural network may be trained remotely and then transmitted processor 108, the model and / or network may be deployed at or processor 108 in any manner described in this disclosure. Additionally, in some embodiments, the machine-learning model and or neural network may be updated to processor 108, the model and / or network may be deployed at or by processor 108 in any manner as described in this disclosure. The machine-learning model and / or network may be deployed / instantiated once trained in any form as described within this disclosure. Feedback from the deployment of the machine-learning model and / or neural network may be turned into new training data, which may be stored either locally and / or transmitted to another device and used for retraining of the model and / or network. Retraining may be administered either remotely or at processor 108. Following retraining of the model and / or network, redeployment / instantiation may be accomplished at or by processor 108 in any manner as described within this disclosure.Further referencing FIG. 1, a regenerated candidate tissue map may be stored in a regenerated candidate tissue map fde, which may be used by downstream applications such as further processing, data storage, and / or high-resolution scanning with large magnification multiples. Regenerated slides data repository may store data of regenerated candidate tissue map. In an embodiment, as described throughout this disclosure, this may be referred to as regenerated candidate tissue map file. In some embodiments, binary masks and tissue maps generated during the process of slide digitization are configured to be low resolution without large magnification multiples.With continued reference to FIG. 1, generating regenerated candidate tissue maps in an inline manner, as described above, may have various advantages. For example, candidate tissue map may be generated based on an image of a slide captured by scanner at a low magnification,49 Atorney Docket No. 1519-174PCT1such as lx magnification, and regenerated candidate tissue map may be generated prior to removing slide from scanner. In turn, scanners may be instructed to carry out higher resolution scanning, such as 40x magnification, of specified regions of interest determined based on regenerated candidate tissue map. In this manner, various inefficiencies may be avoided, such as undesirably carrying out high resolution scanning based on original defective candidate tissue map, resulting in potentially performing high resolution scanning on the wrong regions of slide and / or missing regions that it would have been desirable to scan at high resolution. Additionally, one may avoid untimely detection of errors, such as detection of an error after slide has been removed from scanner and placed back into storage, resulting in potentially significant delays and additional manual processing.Continuing to reference FIG. 1, at least a first algorithm 140 may include an algorithm configured to identify different cell groups in one or more image data 120. In an embodiment, the algorithm may utilize machine or computer vision configured to identify different cell groups. An algorithm configured to identify different cell groups in one or more image data 120 may utilize image classification and / or object detection techniques. For example, and without limitation, in an embodiment, a first algorithm that includes an algorithm configured to identify different cell groups may utilize edge detection, resizing, decimation, interpolation, and / or the like. In some embodiments, specific implementations of an algorithm configured to identify different cell groups may include dividing an image into pieces using one or more of the following: edge detection, average chroma and / or luma value, a grid of rectangles and / or other polygons or even curved shapes. Further, in some embodiments, these divisions may then be run through a classifier using particle swarm optimization, k-means clustering, and / or other clustering and / or unsupervised machine learning process to group pixels into categories. Categories may include chroma and luma. Further clustering may be based on labeled image data 120, for example, and without limitation clusters may be created based on categorization of cell types. Alternatively, the divisions may be input into a neural network that is trained to find the regions of interest in an image. In some embodiments, neural network may include a nuclei detection neural network. Nuclei detection neural networks may include, as non-limiting examples, DenseUNet, UNet, Mask R-CNN neural networks, and the like. In some embodiments, neural network may include one or more tumor localization models which may be configured to locate tumors. In some embodiments, neural networks may include a neural50 Atorney Docket No. 1519-174PCT1network configured to detect mitotic nuclei. An algorithm configured to identify different cell groups may utilize image classification techniques that include supervised and / or unsupervised processes. Further an algorithm configured to identify different cell groups may instantiate machine learning module 132 to identify different cell groups. Machine learning module 132 may include a machine learning model and / or a neural network. In an embodiment, machine learning module 132 may be, or include a convolutional neural network. Exemplary training data 136 may include, without limitation, inputs such as image data 120 containing one or more cell groups, image data 120 containing one or more cell groups and additional background noise, such as bubbles, extra stain, and / or the like, labeled or classified image data 120 containing one cell group, labeled or classified image data 120 containing more than one cell group, and / or the like and correlated to outputs such as image data 120 identifying different cell groups, labeled or classified image data 120 containing one cell group, labeled or classified image data 120 containing more than one cell group. Outputs of machine learning module 132 may be used reiteratively as new training data 136. Training of machine learning module 132 may take place at computing device 104 and / or remotely. Likewise, retraining of machine learning module 132 may take place at computing device 104 and / or remotely.With continued reference to FIG. 1, at least a first algorithm 140 may include an algorithm configured to generate a color gamut correction for one or more image data 120. In an embodiment, an algorithm configured to generate a color gamut correction for one or more image data 120 may configured processor 108 to receive a magnification level, apply a segmentspecific transformation to an individual segment in a first region, a plurality of segmentations of a whole slide image as a function of one or more biological tissue type variabilities and the magnification level, and apply a segment-specific transformation to each segment of the plurality of segments in a first region.Still referring to FIG. 1, processor 108 may be configured to generate a plurality of segmentations of a whole slide image, wherein the whole slide image includes a plurality of biological tissue type variabilities. As used in this disclosure, “segmentation” refers to the process of dividing the image into different parts or regions based on certain criteria. Processor 108 may generate a “plurality of segmentation”, which means it can produce multiple such divisions or categorizations of the WSI. Each segmentation may be based on different criteria, algorithms, or parameters. For example, one segmentation may differentiate tissue types while51 Atorney Docket No. 1519-174PCT1another might be based on cellular structures or staining patterns. As used in this disclosure, “biological tissue type variabilities” refers to variation or difference in the types of biological tissue. Biological tissues may vary widely, from epithelial to connective to muscular tissues. In an embodiment, plurality of biological tissue type variabilities includes at least one of tissue content, tissue morphology, and tissue thickness. A plurality of biological tissue type variabilities suggests that the WSI contains multiple different types of tissue, and there are variations within those tissue types. As used in this disclosure, “slides” refers to a thin flat piece of glass or other transparent material on which a biological sample, such as tissue section or smear, is plated for examination under a microscope. Slides include, but are not limited to histology slides, blood smear slides, cytology slides, bacterial smear slides, frozen section slides, etc. As a non-limiting example, a histology slide containing a biopsy sample from a patient’s liver may be placed onto scanner. Scanner may capture a high-resolution digital image of the slide, image may be transferred through the network, the computing device 104, may initiate the segmentation process. Computer vision models may be used to divide the slide image into multiple segmentations, described further below. One segmentation may delineate areas based on cell density, another may highlight regions based on staining intensity, while yet another isolates potential tumor cells from surrounding tissues.With continued reference to FIG. 1, in another embodiment, the whole slide image is segmented by using a computer vision model. A used in this disclosure, a “computer vision model” refers to a supervised vision model, self-supervised vision model, or any other suitable models those skilled in the art will appreciate. The computer vision model is trained to make predictions based on input data. Computer vision model includes a configuration, which defines a plurality of layers of computer vision model and the relationships among the layers (e.g., input layers, output layers, convolutional layers, densely connected layers, merge layers, and the like). In some embodiments, computer vision model may be configured a deep neural network with at least one hidden layer between the input and output layers. Connections between layers can include feed-forward connections or recurrent connections. One or more layers of computer vision model may be associated with a trained model parameters. As used in this disclosure, a trained model “parameters” are a set of parameters (e.g., weight and bias parameters of artificial neurons) that are learned according to a machine learning process. During the machine learning process, labeled training data is provided as an input to computer vision model, and the values of52 Atorney Docket No. 1519-174PCT1trained model parameters are iteratively adjusted until the predictions generated by computer vision model to match the corresponding labels with a desired level of accuracy.With continued reference to FIG. 1, in some embodiment, the computer vision model may be a pre-trained computer vision model. For example, computer vision model may be trained with a plurality of image representation and magnification level pairs. As described in this disclosure, different “magnification levels” may be associated with corresponding semantic meanings, the trained computer vision model may segment the image representation based on semantic meaning. In some embodiments, computer vision model may be trained to segment the image representation based on one or more of the perceptual characteristics such as brightness, gamma, intensity, and color. In some embodiments, the computer vision model, such as the computer vision model, is trained to segment the image representation based on the semantic meaning. For example, the computer vision model may be trained to segment the image representation to match the semantic meaning of the current magnification level (e.g., the macro structure level, the cell level, the organelle level) of the user, as described in the process. Thus, for example, when the semantic meaning corresponds to the tissue level, the computer vision model is trained to segment the image representation into regions with different tissue types; when the semantic meaning corresponds to the cell level, the computer vision model is trained to segment the image representation into regions with different cell types; and so forth. Improve the visibility for detail at various levels of magnification of a user. For example, the variability in tissue content, thickness, tissue type, etc., may result in a high variance in the perceptual clarity across the image.Still referring to FIG. 1, during execution of processing pipeline, processor 108 executes a computer vision model. Computer vision models are trained to make predictions based on input data. Computer vision models include a configuration. As used in this disclosure, a “configuration” is a plurality of layers of computer vision model and the relationships among the layers. Illustrative examples of layers include input layers, output layers, convolutional layers, densely connected layers, merge layers, and the like. In some embodiments, computer vision models may be configured as a deep neural network with at least one hidden layer between the input and output layers. Connections between layers can include feed-forward connections or recurrent connections.Still referring to FIG. 1, one or more layers of computer vision model may be associated53 Atorney Docket No. 1519-174PCT1with trained model parameters. The trained model parameters are a set of parameters (e.g., weight and bias parameters of artificial neurons) that are learned according to a machine learning process. In some embodiments, the computer vision model may be the supervised vision model or self-supervised vision model. During the machine learning process, labeled training data is provided as an input to computer vision model, and the values of trained model parameters are iteratively adjusted until the predictions generated by computer vision model to match the corresponding labels with a desired level of accuracy. For improved performance, processor 108 may execute computer vision model using a graphical processing unit, a tensor processing unit, an application-specific integrated circuit, or the like.Still referring to FIG. 1, system 100 includes computing device 104 configured to apply a segment-specific transformation to an individual segment in a first region. As used in this disclosure, a “segment-specific transformation” refers to a kind of transformation or adjustment that is applied to a certain segment of an image. For example, a brightness adjustment, contract enhancement, color correction, etc. As described in this disclosure, a “first region” refers to a specific area or portion of the image under consideration. The first region may have multiple segments, and each segment may have its own unique visual characteristics. For example, to ensure that segment within this region have consistent perceptual quality, without vast disparities like one segment being too bright while others are too dim. In some embodiments, processor 108 may automatically choose and execute a segment-specific transformation model which maximizes the perceptual quality in first region. For example, partly due to the difference in thickness, a first segment among the plurality of segments in first region has high brightness while other segments among the plurality of segments in first region have low brightness, the processor may automatically choose the segment-specific transformation for brightness adjustment. Specifically, segment-specific transformation will reduce the brightness of the first segment among the plurality of segments, and / or increase the brightness of other segments among the plurality of segments. In some embodiments, a similar process may be performed accordingly to reduce the high variance in perceptual characteristics among even various parts of the same segment.With continued reference to FIG. 1, in an embodiment, processor 108 may execute the customized segment-specific transformation to the first segment in first region. The customized segment-specific transformation allows the user to have a fine-grained control over the54 Atorney Docket No. 1519-174PCT1transformation. For example, the customized segment-specific transformation may be executed to further improve the perceptual clarity in first region. When the user finds that first region after the segment-specific transformation still has a high variance in perceptual clarity, the user may input feature data for a customized segment-specific transformation, and processor may use the feature data to perform a second-round transformation to the first segment and / or other segments among the plurality of segments to further improve perceptual clarity in first region. In an additional embodiment, segment-specific transformation further includes adjustment to contrast, brightness, gamma, saturation, and red, green, and blue (RGB) values. As a non-limiting example, a histology slide of a tissue sample where certain regions appear washed out due to uneven staining. User may utilize segment-specific transformation to selectively enhance the contrast in these washed-out regions, boost the brightness of darker areas for clearer visualization, adjust the gamma to bring out subtle details in the cellular structures, increase the saturation to highlight differences in tissue types, and fine-tune the RGB values to correct any color imbalances, ultimately resulting in a clearer and more detailed visualization of the tissue sample. In another embodiment, segment-specific transformation is applied in real time. As a non-limiting example, a live microscopic examination of a skin biopsy during a patient consultation. As user moves the slide under the microscope, certain areas may appear too dark or too light due to variations in tissue thickness or uneven lighting. Instead of manually adjusting microscope settings or relying on post-examination software corrections, segment-specific transformation automatically and instantaneously adjusts the contrast, brightness, and color values on the viewed image.With continued reference to FIG. 1, computing device 104 is configured to apply the segment-specific transformation to an individual segment in a second region. As used in this disclosure, a “region” refers to a portion or section of a larger whole, delineated based on specific criteria or characteristics. As used in this disclosure, a “region” refers to a portion or section of a larger whole, delineated based on specific criteria or characteristics. In the context of biological samples or images, region can denote an area with a unique semantic meaning at the tissue level, indicating different types or functions of tissue within the sample. Additionally, a region can also be defined based on differences in thickness, such as areas of a sample or slide where the tissue or material is denser or more sparse compared to surrounding sections. In an embodiment, system further includes receiving feature data from a user as input. For example, In55 Atorney Docket No. 1519-174PCT1some embodiments, the segment-specific transformation model may further transform the second segment into a second processed segment by reducing its brightness. This may help in achieving a uniform visual perception across segments. System further includes applying a second segment-specific transformation to each segment of the plurality of segmentation in first region of the whole slide image to manifest details of each segment in first region. For example, the transformed first segment and the transformed second segment have reduced variability in brightness and contrast, leading to a more consistent visual representation. By focusing the transformation at the segment level (e.g., the tissue level) rather than applying it to the entire image, the natural color gradient of different regions may be preserved.With continued reference to FIG. 1, system 100 for color gamut normalization for pathology slide, computing device 104 is configured to retrieve a plurality of discrete magnification levels from a user. As used in this disclosure, “color gamut” is defined as the range of colors which a particular device can produce or record. It is usually shown by an enclosed area of the primary colors of the device on the chromaticity diagram. For example, the primary colors of monitors are red, green, and blue. Additionally, as used in this disclosure, "discrete magnification" is a predefined level or scale of enlargement that allows for detailed observation and analysis of samples or images. Discrete magnification may offer fixed stages or steps of magnification. Each level provides a distinct and separate view, ensuring consistent and reproducible visualization at that particular magnification. This is beneficial in scenarios where standardization and precision are required, such as in medical imaging, where specific magnification levels can be used to observe and analyze microscopic structures consistently and reliably. For instance, in the context of viewing a pathology slide, discrete magnification allows a user to quickly shift from a broader overview to a close-up examination of cellular structures. The precomputation of segment-specific transformations at these discrete levels may ensure users shift between magnifications.With continued reference to FIG 1, computing device 104 is configured to choose as a first magnification level from a plurality of discrete magnification levels and store the plurality of segmentation in a cache. As used in this disclosure, a “cache” is a specialized high-speed storage mechanism designed to provide quick data retrieval, improving system efficiency by reducing the need to fetch data from primary storage areas. Serving as an intermediary layer between primary storage and computation resources, caches store temporary copies of frequently56 Atorney Docket No. 1519-174PCT1accessed or recently retrieved data, anticipating future requests. As a non-limiting example, web caching in internet browsers, where recently or frequently visited web pages are temporarily stored. This allows users to load a previously visited page more swiftly since it can be fetched from the local cache instead of downloading it again from the internet. Another example may be application caching, where mobile and desktop applications often store user preferences, session data, and other recurrent information in caches to reduce loading times and provide a smoother user experience. Systems may dramatically decrease data access times when utilizing cache, thereby enhancing performance and user experience. In an embodiment, the whole slide image is segmented to match the first magnification level of a user may have their corresponding semantic meanings, hence the computer vision model may be executed to segment the image representation to match the semantic meaning of the current magnification level. The computer vision model may be a supervised vision model, a self-supervised vision model, and any other suitable models those skilled in the art will appreciate. When the image representation includes the biological tissue, a computer vision model may be used to perform semantic segmentation that is biologically relevant for the current level of magnification. The image representation may be segmented to match a current magnification level of the user to facilitate viewing of the underlying regions and structures that have relatively better visibility for details at that magnification level. For example, if a user is set at the first level (e.g., 40* magnification), the image representation may be segmented to match the first level (e g., the cell level). As such, different cells in the image representation will be segmented into individual cells. If the user is then set at the other magnification level (e.g., the third level), the image representation may be segmented to match the third level (e.g., the organelle level). As such, different organelles in the image representation will be segmented into individual organelles. With continued reference to FIG. 1, system 100 further includes storing a segment bounding path of the plurality of segmentation and the segment-specific transformation as metadata in conjunction with the whole slide image for training a machine learning model. A "segment bounding path" is a defined boundary, often represented digitally as a sequence of coordinates or points, which encloses or demarcates a specific segment or region within an image. This bounding path helps in identifying and isolating the segment, allowing for precise modifications or analyses of the encapsulated content without affecting the surrounding areas. In the context of medical or pathology slides, the segment bounding path might outline areas of specific tissue types,57 Atorney Docket No. 1519-174PCT1abnormalities, or any other regions of interest. In a non-limiting Example, imagine a whole slide image of a tissue sample containing both healthy cells and tumor cells. The system identifies these different cell types and creates segmentations for them. For each segmentation, a segment bounding path is defined to circumscribe the particular region where these cells are located. This bounding path could be a simple geometric shape, like a circle or rectangle, or a more complex shape that closely follows the contours of the cell cluster. Another example may be a researcher is training a machine learning model to automatically identify and classify different cell types in similar tissue samples. Segment bounding paths and their associated segment-specific transformations, stored as metadata with the whole slide image as training data. For instance, the researcher may apply a transformation to enhance the visibility of tumor cells in a specific region, this information helps the model understand how tumor cells look both in their natural state and post-transformation. Over time, as the model exposed to numerous examples with associated bounding paths and transformations, it may become adept at recognizing and categorizing different cell types in new, unseen slide images.Further referencing FIG. 1, system 100 may be configured to generate display data structure 148. Display data structure 148 may include at least a primary window 152 and an activity window 156. In some embodiments, display data structure 148 may include primary window 152 and one or more activity windows 156. For example, and without limitation see FIG. 5 for a particular implementation of this embodiment. Further, in some embodiments display data structure 148 may include one or more primary windows 152 and / or one or more activity windows 156. The embodiment of display data structure 148 may depend on a user’s inputs and / or preferences. As used throughout this disclosure, “primary window” is the visualization of image data 120 without overlay of metadata. Metadata may include metadata that is descriptive, administrative, and / or structural. For example, and without limitation, metadata may include annotation data 144. Annotation data 144 may include notes, dates, titles, fde sizes, mask overlays, and / or the like. As used throughout this disclosure, “activity window” is the visualization of image data 120 with overlay of metadata. In some embodiments, activity window 156 may further include one or more adaptive overlays with metadata at varying levels of magnification. Adaptive overlays may include transparent masks with overlay information, contours with overlay information, dots of various sizes with information, and / or the like. Adaptive overlays may be at varying magnification levels, such as high magnification,58 Atorney Docket No. 1519-174PCT1intermediate magnification, and / or lower magnification. In some embodiments, display data structure 148 may display primary window 152 and activity window 156 in a side-by-side manner. Wherein primary window 152 and activity window 156 are both shown at interactive display device 160. Further, in some embodiments, display data structure 148 may include adjacent image data 120 to primary window 152 and activity window 156 including altered image data 120. “Altered image data,” as used herein, refers to image data 120 that includes an additional and or different limitation in comparison to the original image data 120. For example, and without limitation, in some embodiments, where image data 120 includes one or more scanned tissue slides, the altered image data 120 may include one or more tissue slides with a different and / or no stain.With continued reference to FIG. 1, in some embodiments, system 100 may further be configured to accept user input 164 at interactive display device 160. User input 164 may include selecting an area of interest of image data 120, zooming in and / or out, panning across image data 120, highlighting, typing, clicking, and / or the like. For example, and without limitation, in some embodiments, system 100 may be further configured to accept user input 164, selecting a region of interest of image data 120, display at primary window 152, the selected region of interest of image data 120, and enable the user, at interactive display device 160, to zoom, pan, or otherwise interact with the region of interest of image data 120. In some embodiments, user input 164 may include the use of image segmentation tools. For example, in some embodiments, system 100 may be further configured to accept user input 164 of multiple segments of interest from display data structure 148, composite a virtual composite image from the selected segments of interest, and display the virtual composite image at primary window 152. In some embodiments, a user may toggle between metadata displayed on activity window 156 at interactive display device 160. This may enable a user to choose particular metadata displayed at primary window 152.

[0001] With continued reference to FIG. 1, system 100 may modify one or more constituent visualization components as a function of user input 164. “Constituent visualization components” for the purposes of this disclosure, are objects represented within each image, for instance within image data 120. For example, constituent visualization components may include specimens located on the slides. In some cases, constituent visualization components may further include debris, annotations, air bubbles, unwanted visible particles captured within the image, an adhesive used to adhere two slides together and the like. In some cases, constituent visualization59 Atorney Docket No. 1519-174PCT1components may include an object within a particular image. In some cases, a user may annotate a particular specimen wherein processor 108 may identify the annotation as a constituent visualization component. In some cases, an image may capture specimen as well as debris wherein the specimen and the debris are identified as virtual constituent components. In some cases, processor 108 may be configured to identify one or more constituent visualization components within an image using metadata. In one or more embodiments, a particular image may contain metadata of the location and / or the borders of a particular specimen. In one or more embodiments, metadata may include information indicating the presence of one or more specimens within an image. In one or more embodiments, metadata may indicate the borders and / or location of one or more specimens. In some cases, metadata may include information associated with annotations, debris and other constituent visualization components. In some cases, another computing device sperate and distinct from system 100 may have been configured to generate metadata, wherein the metadata may include information about the one or more virtual constituent components.In one or more embodiments, an interactive display device 160 may visualize an image with the identified constituent visualization components. In some cases, a user may select a particular constituent visualization component and input a particular location of the constituent visualization components. In some cases, interactive display device 160 may be configured wherein a user may select a particular constituent visualization component through the clicking of a mouse or a button. In some cases, a user may drag a particular constituent visualization component and ‘drop’ it to a relative location on the image. In some cases, processor 108 may associate the release of the clicking a mouse as a drop. In some cases, the location of the mouse when released may indicate the location of the constituent visualization component. In some cases, processor 108 may isolate constituent visualization component and move it to another location as a function of the dragging and dropping. In some cases, a user input 164 may include a keyboard and any other devices as described herein wherein a user may signify to system 100 that a particular constituent visualization component has been selected and a particular location has been inputted for the new location of constituent visualization component. In some cases, a user may further crop an image following modification wherein free space surrounding the image may be cropped.With further reference to FIG. 1, interactive display device 160 may be configured to60 Atorney Docket No. 1519-174PCT1display to a user the generated display data structure 148 generated at computing device 104. In some embodiments this may be accomplished via a graphical user interface (GUI) configured to display data structure 148 at interactive display device 160. Interactive display device 160 may be communicatively connected to computing device. User input 164 may update display data structure 148. For example, and without limitation, updates to primary window 152 may occur based on interaction with activity window 156 and its associated metadata. In some embodiments, this may be accomplished using event handlers. Interactive display device 160 may be any display device as described throughout this disclosure.Still referring to FIG. 1, in some embodiments, computing device 104 may be configured to configure interactive display device 160 to display an event handler graphic corresponding to a data-reception event handler. As used in this disclosure, an “event handler graphic” is a graphical element with which a user of remote device may interact to enter data, for instance and without limitation for a search query or the like as described in further detail below. An event handler graphic may include, without limitation, a button, a link, a checkbox, a text entry box and / or window, a drop-down list, a slider, or any other event handler graphic that may occur to a person skilled in the art upon reviewing the entirety of this disclosure. An “event handler,” as used in this disclosure, is a module, data structure, function, and / or routine that performs an action on remote device in response to a user interaction with event handler graphic. For instance, and without limitation, an event handler may record data corresponding to user selections of previously populated fields such as drop-down lists and / or text auto-complete and / or default entries, data corresponding to user selections of checkboxes, radio buttons, or the like, potentially along with automatically entered data triggered by such selections, user entry of textual data using a keyboard, touchscreen, speech-to-text program, or the like. Event handler may generate prompts for further information, may compare data to validation rules such as requirements that the data in question be entered within certain numerical ranges, and / or may modify data and / or generate warnings to a user in response to such requirements. An event handler may convert data into expected and / or desired formats, for instance such as date formats, currency entry formats, name formats, or the like. Event handler may transmit data from remote device to computing device 104.In an embodiment, and continuing to refer to FIG. 1, event handler may include a crosssession state variable. As used herein, a “cross-session state variable” is a variable recording data61 Atorney Docket No. 1519-174PCT1entered on remote device during a previous session. Such data may include, for instance, previously entered text, previous selections of one or more elements as described above, or the like. For instance, cross-session state variable data may represent a search a user entered in a past session. Cross-session state variable may be saved using any suitable combination of client-side data storage on remote device and server-side data storage on computing device 104; for instance, data may be saved wholly or in part as a “cookie” which may include data or an identification of remote device to prompt provision of cross-session state variable by computing device 104, which may store the data on computing device 104. Alternatively, or additionally, computing device 104 may use login credentials, device identifier, and / or device fingerprint data to retrieve cross-session state variable, which computing device 104 may transmit to remote device. Cross-session state variable may include at least a prior session datum. A “prior session datum” may include any element of data that may be stored in a cross-session state variable. An event handler graphic may be further configured to display the at least a prior session datum, for instance and without limitation auto-populating user query data from previous sessions.With continued reference to FIG. 1, system 100 may include a computing device. Computing device 104 includes a processor communicatively connected to a memory. 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,62 Atorney Docket No. 1519-174PCT1radio 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.Further referring to FIG. 1, Computing device 104 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 104 may include, be included in, and / or communicate with a mobile device such as a mobile telephone or smartphone. Computing device 104 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 104 may interface or communicate with one or more additional devices as described below in further detail via a network interface device. Network interface device may be utilized for connecting computing device 104 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 (c.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 104 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 104 may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. Computing device 104 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,63 Atorney Docket No. 1519-174PCT1redundantly, or in any other manner used for distribution of tasks or memory between computing devices. Computing device 104 may be implemented, as a non-limiting example, using a “shared nothing” architecture.With continued reference to FIG. 1, computing device 104 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 104 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 104 may perform any step or sequence of steps as described in this disclosure in parallel, such 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.Now referring to FIG. 2, illustrated is a particular implementation of a system 200 for augmented visualization using activity windows, wherein a user may highlight a portion of the image data. In an embodiment, a user may highlight a certain portion of the image data within activity window 204. The highlighted portion of image data, or the highlighted subset of the image data is displayed in primary window 208. The subset image data displayed in primary window 208 may be zoomed in and out based on the resolution levels available to the image data. Panel set A, illustrates the bottom right portion of the image data being highlighted in activity window 204. The corresponding subset image is displayed in primary window 208. Likewise, panel set B illustrates the top left portion of image data being highlighted in activity window 204. The corresponding subset image data is displayed in primary window 208.Now referring to FIG. 3, illustrated is a particular implementation of a system 300 for64 Atorney Docket No. 1519-174PCT1augmented visualization using activity windows, wherein a user may select a segment of image data to view. Selection of a segment from activity window 304 will display only the image data contents of that segment in primary window 308. Illustrated in activity window 304, is image data being segmented into five parts: 312, 316, 320, 324, and 328. Part 316 was selected by the user in activity window 304. Therefore, only part 316 is displayed in primary window 308. Pan, zoom, and / or other user interface options are available for part 316 in primary window 308.Now referring to FIG. 4, illustrated is a particular implementation of a system 400 for augmented visualization using activity windows, wherein selected segments from the activity window 404 are shown. A user may select segments from image data from different locations of the image data, as shown in FIG. 4. The segments may be spatially reorganized as a virtual slide in primary window 408 in a compact representation of segments for ease of analysis.Now referring to FIG. 5, illustrated is a particular implementation of a system 500 for augmented visualization using activity windows, wherein selected segments from multiple activity windows are shown. In some embodiments a user may select segments of image data from one or more activity windows, such as activity window 504 and activity window 508, having one or more image data present. The segments may be spatially reorganized as a virtual slide in primary window 512 in a compact representation of segments of interest for ease of analysis. Pan, zoom, and / or other user interface options as discussed throughout this disclosure are available for the selected segments displayed in primary window 512.FIGS. 6-1 IB are simplified diagrams of a data flow 600 for inline quality control according to some embodiments. FIG. 6 provides an overview of data flow 600, and FIGS. 7- 1 IB illustrate certain modules of data flow 600 in greater detail. In some embodiments consistent with FIG. 1, data flow 600 may be implemented using inline quality control program in conjunction with other components and / or features of system 100, as further described below.Referring now to FIG. 6, in general, data flow 600 includes a set of modules 610-660 (described in further detail below) that correspond to steps in the slide digitization process. Within each module, inline quality control methods are in place to trap errors that can be fixed at that stage below an acceptable threshold for downstream processing. Additionally, or alternately, the inline quality control methods associated with each module may accumulate deviations of observed features from acceptable thresholds and pass the deviations to the next module. In this sense, data flow 600 conceptually resembles a directed computation graph with directed edges65 Atorney Docket No. 1519-174PCT1leading back to the beginning with information to perform a second pass selectively to fix errors. Most nodes in the graph have automatically determined quality acceptance tests baked in, with tuned thresholds that determine if the processed image proceeds to the next stage or is fed back to the beginning to correct for specific errors detected. For images that are finally output, the errors are within acceptable thresholds. Additionally, the errors that are present in those slides are demarcated for downstream algorithms to avoid or fix. For instance, generative models like diffusion models can leverage these delineated regions to perform inpainting which is then used for downstream tasks.Still referring to FIG. 6, in some embodiments, system 100 performs a scan of a slide. System 100 may perform a scan of a slide by capturing at least a first image and capturing at least a second image. Performing a scan may include identifying a first scanning parameter. As used herein, a “scanning parameter” is a setting at which a device captures an image. In nonlimiting examples, a scanning parameter may include a focal depth, a (x,y) position, a magnification level, an aperture, a shutter speed, an ISO sensitivity, and a level of backlighting. In some embodiments, a first scanning parameter may include which of a plurality of optical sensors to use to capture an image. For example, first scanning parameter may configure a device to capture a macro image of a whole slide and / or an entirety of a sample on a slide. As used herein, a “macro image” is an image in which entirety of a sample on a slide is in frame. In another example, at least a first image includes an image of a region of a sample. Such a macro image may be a wider angle image than subsequently captured images. Such a macro image may include a lower resolution image than a subsequently captured image. Such a macro image may be used to determine subsequent scanning parameters, as described herein. In some embodiments, a macro image may be captured at lx magnification. In some embodiments, a macro image may be captured using a 5 megapixel camera.Still referring to FIG. 6, in some embodiments, performing a scan of a slide may include, using an optical system, capturing at least a first image as a function of a first scanning parameter. In some embodiments, at least a first image may be captured at a first position as a function of first scanning parameter. In some embodiments, capturing at least a first image of a slide may include using actuator mechanism and / or actuator controls to move optical system and / or slide into desired positions.Still referring to FIG. 6, in some embodiments, system 100 may determine a first quality66 Atorney Docket No. 1519-174PCT1metric as a function of at least a first image. As used herein, a “quality metric” is a datum describing an assessment of a quality of an image, a quality of a section of an image, a quality of a feature of an image, or a combination thereof. In a non-limiting example, a quality metric may describe a degree to which an image and / or a section of an image is in focus. Non-limiting examples of quality metrics include localization quality metric, focus sampling quality metric, biopsy plane estimation quality metric, z-stack acquisition quality metric, and stitching quality metric, each of which is described further herein.Still referring to FIG. 6, in some embodiments, system 100 may determine a second scanning parameter as a function of first quality metric. System 100 may, using an optical system, capture at least a second image as a function of second scanning parameter. Examples of quality metrics, and their associated scanning parameters are described further below. In some embodiments, at least a second image may be made up of a plurality of high resolution images. Such high resolution images may be stitched together to construct at least a second image which covers a desired area, such as an entire sample or region of interest. In some embodiments, at least a first image may be captured at a first location, and at least a second image may be captured at a second location. For example, a first location may be determined as a function of first scanning parameter, and may include a default location, a location input by a user, or the like. As an example, a second location may be determined as a function of a localization module as described further herein.Still referring to FIG. 6, in some embodiments, system 100 may determine multiple quality metrics. For example, system 100 may determine localization quality metric, focus sampling quality metric, biopsy plane estimation quality metric, z-stack acquisition quality metric, and stitching quality metric simultaneously. In some embodiments, system 100 may determine one or more scanning parameters and / or capture one or more images based on such quality metrics. For example, system 100 may first determine localization quality metric, then may determine a scanning parameter and may capture a subsequent image using such scanning parameter. System 100 may then determine focus sampling quality metric, may determine a subsequent scanning parameter, and may capture a subsequent image using such scanning parameter.Referring now to FIGS. 6 and 7A-7B, first quality metric may include a localization quality metric; and determining second scanning parameter may include identifying at least a67 Atorney Docket No. 1519-174PCT1region of interest of slide 704. As used herein, a “region of interest” is a specific area within a slide, or a digital image of a slide, in which a feature is detected. A feature may include, in nonlimiting examples, debris 708 sample 712, writing on a slide, a crack in a slide, a bubble, and the like. In some embodiments, a feature includes a sample, such as a biological sample. As used herein, a “localization quality metric” is a quality metric which assesses the performance of a localization module. In some embodiments, localization quality metric may include one or more composite measures used to evaluate the precision and reliability of localization module’s ability to detect and position the sample within an image. In other embodiments, localization quality metric may include one or more quantifiable measures utilized by localization module within the system designed to identify region of interest 716. As shown in FIGS. 6 and 7A-7B, data flow 600 includes a localization module 610. As used herein, a “localization module” is an automatic system for identifying a region of interest. Localization module 610 receives an image 710 of slide 704 (shown in FIG. 7A), such as one of images discussed in reference to image data 120 in FIG. 1. In some embodiments, image 710 may be captured at a low magnification level (e.g., lx) and the field of view may cover a substantial portion of slide 704 (e.g., the image may include most of slide 704, including regions containing the biomedical specimens).Still referring to FIGS. 6 and 7A-7B, based on image 710, localization module 610 localizes regions of interest 716 on slide 704. For example, as illustrated by image 720, localization module 610 may identify portions of slide 704 that contain biomedical specimens and other slide attributes including but not limited to the color intensity of stained tissue (e g., dark or faint), the size, shape, and position of the specimen, debris, cover slip boundaries, annotations (e.g., handwritten text, printed text, scanning codes, arrows and markings, or the like), or the like. Localizing regions of interest 716 on slide 704 may be performed by a variety of suitable image processing techniques, including techniques that use machine learning models trained to identify the selected regions of interest. Annotations may be identified using, for example, an optical character recognition (OCR) system.Still referring to FIGS. 6 and 7A-7B, based on the identified areas of interest, localization module 610 may select one or more portions of the slide to be scanned at a higher magnification level (e.g., lOx, 20x, or 40x magnification). For example, localization module 610 may identify one or more bounding boxes 732 that represent the portions of the slide to be scanned at the higher magnification level, e.g., portions containing the biomedical specimen. Localization68 Atorney Docket No. 1519-174PCT1module 610 may then form a grid 734 (or other suitable pattern) for each bounding box 732 that identifies the x, y, and / or z coordinates of the desired images to be captured at the higher resolution. In addition, localization module 610 may identify a scanning pattern 736 (e.g., a raster scanning pattern having a primary scanning direction and a secondary scanning direction) that specifies a particular sequence in which the desired images are to be acquired. In some embodiments, localization module 610 may identify one or more features during localization. Such features may include, in non-limiting examples, samples, bubbles, debris, and annotations. In some embodiments, localization module 610 may determine a localization module confidence score. As used herein, a “localization module confidence score” is a data structure describing a likelihood that a feature of a particular category is present in a particular location, region, or both. For example, a first localization module confidence score may be determined with respect to a particular region of a slide, where the first localization module confidence score describes the likelihood that the region contains an annotation. In this example, a second localization module confidence score may be determined with respect to a particular region of a slide, where the second localization module confidence score describes the likelihood that the region contains a biological sample. In some embodiments, localization module 610 may identify a region of interest as a function of one or more localization module confidence scores. For example, localization module 610 may identify a region of interest as a function of a localization module confidence score describing the likelihood that a biological sample is present. In some embodiments, high magnification probing may be used to determine whether a feature, such as a biological sample, is present. For example, high magnification images may be captured of a plurality of regions of a slide, and such high magnification images may be analyzed to determine whether a feature (such as a biological sample) is present. In some embodiments, high magnification probing may be used to determine a region of interest which does not include a region with a low probability of containing a feature such as a biological sample.Still referring to FIGS. 6 and 7A-7B, in some embodiments, localization module 610 may select one or more reference points 738 within grid 734 for further imaging and analysis, such as biopsy plane estimation or focus sampling, as further described below. As used herein, a “reference point” is a point within a grid whose focal distance, determined by one or more predetermined criteria, is used to calculate the focal distance of another point. In some embodiments, the optimal focal distance of a reference point is used to calculate the optimal69 Atorney Docket No. 1519-174PCT1focal distance of a different point. For example, reference point 738 may correspond to a point at the center of bounding box 732, a point having the maximum color intensity within bounding box 732, a point determined to be the most likely to contain a biomedical specimen (e.g., as determined using a machine learning model), or another suitable selection criteria.Still referring to FIGS. 6 and 7A-7B, in some embodiments, localization module 610 may include one or more inline quality controls. For example, and without limitation, localization module 610 characterizes the content present on the slide, may check that image 710 is in focus, that image 710 includes the biological specimen within the field of view, that an amount of debris on the slide or other unwanted artifacts or noise in image 710 does not exceed a predetermined threshold, that a confidence level associated with identifying areas of interest exceeds a predetermined threshold, or the like. When one or more checks returns a negative result, one or more remedial actions may be taken, such as recapturing image 710 with different exposure settings, bringing image processing / ML algorithms to enhance the image etc., indicating to a lab admin the slide preparation, or the like. In some embodiments, the inline quality checks may output information associated with the results of the quality checks, such as a numerical score or an indication of areas of a slide that were found to be low quality, that can be used by subsequent modules in data flow 600.Still referring to FIGS. 6 and 7A-7B, in some embodiments, the areas of interest identified by localization module 610 may be provided to downstream applications for further processing. For example, localization module 610 may provide a mask to delineate areas of interest with artifacts that should be avoided, inpainted, or otherwise handled by the downstream application.Referring now to FIGS. 6 and 8, first quality metric may include biopsy plane estimation quality metric; and determining second scanning parameter may include identifying a plane within a region of interest which contains a biological specimen. As used herein, a “biopsy plane estimation quality metric” is a quality metric which assesses the performance of a biopsy plane estimation module. In some embodiments, biopsy plane estimation quality metric may include one or more composite measures used to evaluate the precision and reliability of biopsy plane estimation module’s ability to identify a plane within a region of interest which contains a biological specimen. In other embodiments, localization quality metric may include one or more quantifiable measures utilized by localization module within the system designed to identify a70 Atorney Docket No. 1519-174PCT1plane within a region of interest which contains a biological specimen. As depicted in FIGS. 6 and 8, data flow 600 may include a biopsy plane estimation module 620. As used herein, a “biopsy plane estimation module” is an automatic system for identifying a plane within a region of interest which contains a biological specimen. Biopsy plane estimation module 620 instructs a scanner 810 (shown in FIG. 8) to perform a scan along the z-axis of a slide 820 at high magnification to identify the plane that contains the biomedical specimen of interest (e.g., a biopsy). For example, scanner 810 may correspond to a scanner. Scanner 810 may acquire a series of images 830 along the z-axis using the same high magnification objective lens (e.g., lOx, 20x, or 40x) that will ultimately be used to acquire images used for the digitized slide. One or more attributes of the series of images 830 (e.g., color intensity) may be analyzed to estimate the plane containing the biopsy (or other biomedical specimen), as shown illustratively at process 840. For example, the biopsy plane may correspond to the z-axis coordinate that produces an image with the maximum color intensity.Still referring to FIGS. 6 and 8, in some embodiments, biopsy plane estimation module 620 may perform biopsy plane estimation at x and y coordinates corresponding to reference point 738 (shown in FIG. 7B), e.g., a point determined by a localization module to be optimal for biopsy plane estimation. Performing biopsy plane estimation at reference point 738 may improve efficiency by ensuring that the biopsy plane estimation is performed at a location likely to contain a biomedical specimen. For example, the likelihood that biopsy plane estimation is repeated at different locations on the sample, or the likelihood that manual intervention is used to select or adjust the location for biopsy plane estimation, is reduced.Still referring to FIGS. 6 and 8, in some embodiments, biopsy plane estimation module 620 may include one or more inline quality controls. For example, biopsy plane estimation module 620 may check that the biopsy plane estimation is performed at a location that contains a sufficient amount of the biomedical specimen to accurately perform biopsy plane estimation. When the check returns a negative result, one or more remedial actions may be taken, such as selecting a different location to perform biopsy plane estimation, generating an alert that insufficient biomedical specimen was detected, or the like. In some embodiments, biopsy plane estimation module 620 may output information associated with the results of the quality checks, such as a numerical score, that can be used by subsequent modules in data flow 600. The information may be aggregated with information supplied by preceding modules in data flow71 Atorney Docket No. 1519-174PCT1600 (e.g., localization module 610) to provide a running indicator of the quality of the slide digitization process, which may trigger further remedial actions (e.g., rescans or alerts) when the running indictor exceeds a predetermined threshold.Referring now to FIGS. 6 and 9, first quality metric may include focus sampling quality metric; and determining second scanning parameter may include identifying a focal setting at which a selected point is in focus. As used herein, a “focus sampling quality metric” is a quality metric which assesses the performance of a focus sampling module. In some embodiments, focus sampling quality metric may include one or more composite measures used to evaluate the precision and reliability of focus sampling module’s ability to identify a focal setting at which a selected point is in focus. In other embodiments, focus sampling quality metric may include one or more quantifiable measures utilized by focus sampling module within the system designed to identify a focal setting at which a selected point is in focus. As depicted in FIGS. 6 and 9, data flow 600 may include a focus sampling module 630. As used herein, a “focus sampling module” is an automated system for identifying a focal setting at which a selected point is in focus. Focus sampling module 630 may carry out focus optimization at two or more sample points 912 and 914 (shown in FIG. 9) within the scanning grid (e.g., grid 734). Sample points 912 and 914 may be selected in relation to a reference point 920 (e.g., reference point 738). For example, sample points 912 and 914 may be positioned along a same row of the scanning grid as reference point 920. At each of sample points 912 and 914, focus sampling module 630 may instruct the scanner to determine optimal focus settings for subsequent imaging. For example, the optimal focus settings at sample points 912 and 914 may be determined using a variety of known techniques for auto-focusing, e.g. sharpness of the image. When the optimal focus settings at sample points 912 and 914 are different, focus sampling module 630 may determine a gradient (or other suitable functional relationship) of the focus settings such that optimal focus settings for the remaining points in the scanning grid may be estimated, e.g., by interpolation. Focus sampling may therefore provide an efficient technique for estimating the optimal focus settings throughout the scanning grid by performing optimization at a subset of the points in the grid and using gradients to estimate the optimal settings at the remaining points.Still referring to FIGS. 6 and 9, in some embodiments, focus sampling module 630 may include one or more inline quality controls. For example, focus sampling module 630 may check that the focus sampling is performed at locations that contain a sufficient amount of the72 Atorney Docket No. 1519-174PCT1biomedical specimen to accurately determine the focus settings. This may be performed by estimating the quality of specimen (e.g. thickness of specimen in z-direction), checking attributes of the specimen such as whether the specimen present on the glass slide corresponds to tissue versus annotation (e.g., pen marks), and confirming planarity of the slide estimated. Such locations may include, in non-limiting examples, fields of view of high resolution images of sections of a slide. When such a check returns a negative result, one or more remedial actions may be taken, such as selecting different locations to perform focus sampling, generating an alert that insufficient biomedical specimen was detected, or the like. In some embodiments, focus sampling module 630 may output information associated with the results of the quality checks, such as a numerical score, that can be used by subsequent modules in data flow 600. The information may be aggregated with information supplied by preceding modules in data flow 600 (e.g., modules 610-620) to provide a running indicator of the quality of the slide digitization process, which may trigger further remedial actions (e.g., rescans or alerts) when the running indictor exceeds a predetermined threshold. In some embodiments, focus sampling inline quality controls may include identifying locations where focus sampling errors are present. For example, locations which are out of focus may be identified, and remedial actions (such as rescans) may be taken for those areas. In some embodiments, such locations may include fields of view of high resolution images of sections of a slide.Referring now to FIGS. 6 and 10A-10B, first quality metric may include z-stack acquisition quality metric; and determining second scanning parameter may include using the optical system, capturing a z-stack at a selected point. As used herein, a “z-stack acquisition quality metric” is a quality metric which assesses the performance of a z-stack acquisition module. As depicted in FIGS. 6 and 10A-10B, data flow 600 may include a z-stack acquisition module 640. Based on information provided by preceding modules 610-640 (e g., the scanning pattern, biopsy plane, and focus settings), z-stack acquisition module 640 instructs a scanner 1010 (shown in FIGS. 10A-10B) to acquire a set of images 1020 (referred to as a z-stack) along the z-axis of a slide at high magnification. As shown in FIG. 10B, the images in the z-stack may be acquired by sweeping the depth of field using an objective lens of scanner 1010. In this manner, features of the biomedical specimen (e.g., the tissue sample) that vary along the z-axis, such as bumps, ridges, and other 3 -dimensional features, may come into focus in different layers of the z-stack. In some embodiments, z-stack acquisition module 640 may output an aggregate73 Atorney Docket No. 1519-174PCT1image based on the set of images in the z-stack. For example, as illustrated in FIG. 10B at process 1030, the aggregate image may correspond to a selected image from the z-stack that contains the most in-focus content among the images in the set. Other suitable techniques for aggregating the z-stack images may be used. Z-stack acquisition module 640 may acquire z-stack images at each x-y location in the sample to be scanned (e.g., as determined by localization module 610).Still referring to FIGS. 6 and 10A-10B, in some embodiments, z-stack acquisition module 640 may include one or more inline quality controls. For example, z-stack acquisition module 640 may check that the amount of in focus image content in the z-stack exceeds a predetermined threshold, that the amount of debris or imaging artifacts in the images is below a predetermined threshold, that the levels of tissue folding and / or slide preparation artifacts are below a predetermined threshold, that the tissue is inside / outside the coverslip or the like, that the detected specimen corresponds to tissue rather than an annotation, or the like. When one or more of these checks return a negative result, one or more remedial actions may be taken, such as reacquiring the z-stack, adjusting the position of the z-stack (e.g., capturing images at a different position along the z-axis), adjusting the z stack size, generating an alert, or the like.Still referring to FIGS. 6 and 10A-10B, in some embodiments, z-stack acquisition module 640 may track quality indicators associated with each z-stack as scanning proceeds throughout the scanning grid. For example, z-stack acquisition module 640 may count the locations in the scanning grid for which the quality checks produced negative results and may perform a remedial action when the count exceeds a predetermined threshold. In some embodiments, z-stack acquisition module 640 may generate and display a map indicating the quality of each z-stack in the scanning grid.Still referring to FIGS. 6 and 10A-10B, in some embodiments, z-stack acquisition module 640 may output information associated with the results of the quality checks, such as a numerical score or a map indicating the quality of the z-stack scan at each location in the slide. This information can be used by subsequent modules in data flow 600. The information may be aggregated with information supplied by preceding modules in data flow 600 (e.g., modules 610- 630) to provide a running indicator of the quality of the slide digitization process, which may trigger further remedial actions (e.g., rescans or alerts) when the running indictor exceeds a predetermined threshold.74 Atorney Docket No. 1519-174PCT1Referring now to FIGS. 6 and 11 A-l IB, performing a scan of a slide may include, using an optical system, capturing at least a second image as a function of second scanning parameter. In some embodiments, capturing at least a second image may include capturing a plurality of images, and stitching such images together. In some embodiments, performing a scan of a slide may include determining a stitching quality metric as a function of the at least a second image. In some embodiments, performing a scan of a slide may further include determining a third scanning parameter as a function of the stitching quality metric; using the optical system, capturing a second plurality of images as a function of the third scanning parameter; and combining the second plurality of images to create at least a third image. For example, if stitching quality metric is determined such that severe stitching related errors are identified, then additional images may be captured of an affected region, and such images may be used to assemble a corrected image. In some embodiments, image data from both at least a second image and subsequently captured image data is used to assemble at least a third image. As used herein, a “stitching quality metric” is a quality metric which assesses the performance of a stitching module. In some embodiments, stitching quality metric may include one or more composite measures used to evaluate the precision and reliability of stitching module’s ability to assemble a larger image from a plurality of images. In other embodiments, localization quality metric may include one or more quantifiable measures utilized by localization module within the system designed to assemble a larger image from a plurality of images. As depicted in FIGS. 6 and 11 A- 1 IB, data flow 600 may include a stitching module 650, implementation of which is illustrated in diagram 1100. As used herein, a “stitching module” is an automated system for assembling a larger image from a plurality of images. Stitching module aligns and stitches together (also referred to as pasting or fusing) adjacent images within the scanning grid 1110 (shown in FIG. 11A) to form an aggregated output image. In an ideal scenario, each pair of adjacent images acquired by the scanner (e.g., images 1112 and 1114) is aligned without observable gaps, overlaps, or offsets between the images, including in the x, y, and z directions. In practice, however, such defects may be present and may result in artifacts such as ghosting (e.g., regions that are blurred due to stitching errors). Another type of artifact that may arise due to stitching is the visual effect of banding, as illustrated in FIG. 1 IB. In an image with banding 1122, artifacts such as vertical lines may appear along the axes corresponding to the scanning grid. Whereas in an imaging without banding 1124 a clearer image is shown.75 Atorney Docket No. 1519-174PCT1Still referring to FIGS. 6 and 11 A-l IB, in some embodiments, stitching module 650 may include one or more inline quality controls to address stitching errors, such as ghosting artifacts and displacement. For example, stitching module 650 may check that the presence of such artifacts in the output image is below a predetermined threshold. When the threshold is exceeded, one or more remedial actions may be taken, such as rescanning portions of the slide where the artifacts were present, or the like. Additional remedial actions may include triggering a noninline stitching process. For example, A scanning process may be completed, and a subsequent stitching process may be started. In some embodiments, the inline quality checks may output information associated with the results of the quality checks, such as a numerical score indicating the level of stitching artifacts, that can be used by subsequent modules in data flow 600. The information may be aggregated with information supplied by preceding modules in data flow 600 (e.g., modules 610-640) to provide a running indicator of the quality of the slide digitization process, which may trigger further remedial actions (e.g., rescans or alerts) when the running indictor exceeds a predetermined threshold.Returning to FIG. 6, performing a scan may further include determining a second quality metric as a function of at least a first image; determining a combination quality metric as a function of first quality metric and second quality metric; determining a third scanning parameter as a function of the combination quality metric; and using optical system, capturing at least a third image as a function of second scanning parameter. Data flow 600 may include a whole slide assessment module 660. As used herein, a “whole slide assessment module” is an automated system for analyzing an output image generated by a stitching module. Whole slide assessment module 660 may perform a variety of quality checks on the output image and / or may synthesize the results of quality checks performed by modules 610-650. For example, whole slide assessment module 660 may perform a final quality check on the output image to trap out of focus, stitching, banding, missed specimen, z-stack shift errors (x,y shift across the image along the z plane of stack), or the like. When the check produces a negative result, one or more remedial actions may be taken such as rescanning portions of the slide where the errors were detected. In some embodiments, the slide may remain in the scanner during these quality checks, such that taking a remedial action (e.g., rescanning a portion of the slide) involves little or no additional setup time.Still referring to FIG. 6, in some embodiments, whole slide assessment module 660 may76 Atorney Docket No. 1519-174PCT1generate or compile a representation of the slide (e.g., a map or other suitable representation) that delineates portions of the output image in which artifacts persist but are within an acceptable threshold (e.g., portions in which errors are detected but not fully resolved by the quality controls identified above). The representation may enable the downstream application to handle the remaining artifacts in a suitable manner, e g., by avoiding or applying inpainting techniques to the portions of the image that contain artifacts.Still referring to FIG. 6, in some embodiments, performing a scan of a slide may further include algorithmically removing a banding error from an image such as at least a second image. In some embodiments, banding errors may be a product of non-uniform lighting across images and / or regions of images.Still referring to FIG. 6, in some embodiments, an error may be flagged. For example, an error may be flagged based on a quality metric. In some embodiments, an error may be flagged as a function of a focus sampling quality metric. In some embodiments, an error may be flagged as a function of a stitching quality metric. In some embodiments, an error may be flagged if rescanning a slide and / or a region of a slide does not sufficiently lead to a reduction in a quality metric. In some embodiment, prevalence and / or severity of errors may be tracked using a map. For example, a map may depict sections of an image and quality metrics associated with such sections of the image. For example, a map may include an overlay over an image which is color coded according to a particular quality metric and / or an aggregate measure of multiple quality metrics. In some embodiments, a map may be used to expedite manual quality control of an image. For example, a map may be output to a user using a display as described below in order to aid the user in a quality control process.Still referring to FIG. 6, in some embodiments, system 100 may transmit one or more images and / or maps 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. 6, in some embodiments, system 100 may determine a visual element data structure. In some embodiments, system 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 at least a first image, at least a second image, at least a third image, a map, and elements77 Atorney Docket No. 1519-174PCT1of a GUI.Still referring to FIG. 6, 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 at least a first image, at least a second image, at least a third image, elements of a GUI, first scanning parameter second scanning parameter, third scanning parameter, first quality metric, and stitching quality metric. In a non-limiting example, a visual element data structure may be generated such that visual element depicting at least a second image is displayed to a user.Still referring to FIG. 6, 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 magnification level.Still referring to FIG. 6, 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 a scanning parameter to be displayed when a user selects an image captured using that scanning parameter using a GUI.Still referring to FIG. 6, 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 images and / or GUI elements may be displayed simultaneously.Still referring to FIG. 6, in some embodiments, system 100 may transmit visual element to a display such as output interface. 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 some78 Atorney Docket No. 1519-174PCT1embodiments, a visual element data structure is determined as a function of data input by user into a display.Still referring to FIG. 6, 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, a quality metric data structure may include an int value representing a degree to which an image is in focus. In some embodiments, data in a data structure may be organized in a linked list, tree, array, matrix, tenser, and the like. 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 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.Now referring to FIG. 12, a box diagram depicting an exemplary embodiment of a system 1200 for digitizing a slide is provided. System 1200 may include processor 1204, and memory 1208 containing instructions 1212 configuring processor 1204 to perform one or more processes described herein. Computing device 1216 may include processor 1204, and memory 1208, and may interact with other elements of system 1200, such as by configuring elements of an optical system to capture images, configuring an actuator mechanism to move, configuring a user interface to display information and the like. System 1200 may further include slide 1220, such as a glass slide containing a biological sample. Optical sensor 1224 may be used to capture one or more images of slide 1220. Actuator mechanism 1228 may be used to move optical sensor 1224, slide port 1244, and / or slide 1220 into correct positions for images to be captured according to desired parameters. System 1200 may further include input interface 1232, such as a mouse and keyboard, output interface 1236 such as a screen and speaker system, and user interface 1240 which includes input interface 1232 and output interface 1236. Computing device 1216 may use settings according to first scanning parameter 1248 to capture at least a first image 1252. At least a first image 1252 may be used to determine first quality metric 1256. First quality metric 1256 may include localization quality metric 1260, biopsy plane estimation quality metric 1264, focus sampling quality metric 1268, and / or z- stack acquisition quality metric 1272. First quality metric 1256 may be used to determine second scanning parameter 1276. Settings of79 Atorney Docket No. 1519-174PCT1second scanning parameter 1276 may be used to capture at least a second image 1280. Stitching quality metric 1284 may be determined as a function of at least a second image 1280, such as in a case in which second image 1280 is assembled from a plurality of images each covering narrower regions of slide 1220. Third scanning parameter 1288 may be determined as a function of stitching quality metric 1284, and at least a third image 1292 may be captured using settings of third scanning parameter 1288. One or more of at least a first image 1252, at least a second image 1280, and at least a third image 1292 may be displayed using user interface 1240. In a non-limiting example, system 1200 may capture a macro image which includes in frame the entirety of a biological sample which a user desires a high resolution image of. In this example, system 1200 may use this macro image to identify one or more parameters for capturing subsequent images as described herein; such parameters may include a region within the macro image which contains the sample, a row within this region in which the sample is present, a focal distance at one or more points are in focus, and the like. In this example, system 1200 may use such parameters to capture a plurality of images and may assemble a high resolution image from this plurality of images. This plurality of images may be checked for errors, such as stitching errors, and these errors may be corrected. A final image may be displayed to a user using a user interface.Now referring to FIG. 13, a diagram 1300 of exemplary serial sections in various cases are shown. During the process of slide digitization, multiple serial sections may be assessed for a patient case and / or tissue block of a patient case. In this embodiment, these serial sections are therefore not independent from each other. Rather, the associations among these serial sections may give rise to patterns, trends, and / or other information that may be leveraged in the slide digitization process. For instance, such information can be utilized to correct a serial section under assessment which may have been contaminated.With further reference to FIG. 13, first serial section set 1304 and a second serial section set 1308 may be related to a first patient case and thereby assigned a unique case identification number associated with the first patient case. The first serial section set 1304 may be acquired from a first tissue block of the first patient case and the second serial section set 1308 may be acquired from a second tissue block of the first patient case. Additionally, in some embodiments, a third serial section set 1312 may be related to a second patient case and thereby assigned another unique case identification number associated with the second patient case. The third80 Atorney Docket No. 1519-174PCT1serial section set 1312 may be acquired from a first tissue block of the second patient case. The first serial section set 1304 may include a first stain slide 1316, a second stain slide 1320, and a third stain slide 1324. The second serial section set 1308 may include a fourth stain slide 1328 and a fifth stain slide 1332. The third serial section set 1312 may include a sixth stain slide 1336 and a seventh stain slide 1340.Continuing to reference FIG. 13, in determining the first tissue block of the first patient case, the first stain slide 1316 may be identified as a reference slide among the first serial section set 1304 that may be used for the slide digitization process described in the present disclosure. In some examples, the first stain slide 1316 identified as a reference slide may be an H&E-stained slide. When a group of slides are processed together, the reference slide may provide information for assessing other slides with other stains. For example, and without limitation, the second stain slide 1320 and the third stain slide 1324 may be utilized as candidate slides.In further reference to FIG. 13, in some embodiments, a non-H&E-stained slide may be identified and used as a reference slide for processing a group of slides. For example, and without limitation, when determining the second tissue block of the first patient case, the second serial section set 1308 is processed as a group for the slide digitization process described in the present disclosure. In the second serial section set 1308, the fourth stain slide 1328 may be a non-H&E-stained slide, which may be identified and used as a reference slide for assessing the fifth stain slide 1332. The fifth stain slide 1332 may be a candidate slide in the second serial section set 1308.Still referencing FIG. 13, in some embodiments, multiple serial sections may be placed on the same slide, which is referred to as an intra-serial section slide. For example, and without limitation, the sixth stain slide 1336 in the third serial section set 1312 is an intra-serial section slide. Alternatively, in embodiments that only one serial section is placed on a slide, the embodiment is referred to as an inter-serial section slide.Now referring to FIG. 14, illustrated is a diagram 1400 of serial sections on different slides at various stages during an exemplary process of slide digitization. Prior to generation of candidate tissue map 1416 and reference tissue map 1412, a candidate binary mask 1408 and a reference binary mask 1404 are resulted from the scanning of serial sections on their respective slides. The candidate binary mask 1408 is initially wrongly assessed due to the presence of debris and / or lack of stain intensity of a portion of the tissue. Alternatively, the reference binary81 Atorney Docket No. 1519-174PCT1mask 1404 appears to be correctly assessed of tissue shape and size by an inline automatic computer program. When the inline registration is completed, the candidate tissue map 1416 is aligned with the reference tissue map 1420. The reference binary mask 1404 is used to correct the candidate binary mask 1408. As a result, an aligned candidate tissue map 1424 is generated. The aligned candidate tissue map 1424, which is based on the corrected candidate binary mask 1404, may then be used to generate a regenerated candidate tissue map 1432. Following the process of regeneration of the candidate tissue map, the regenerated candidate tissue map 1432 appears to represent tissue with similar shape and size as the reference tissue map 1428. Alternatively, the orientation and the relative location on the glass slide appears to be different between the regenerated candidate tissue map 1432 and the reference tissue map 1428.Now referring to FIG. 15, illustrated is a diagram 1500 of serial sections on the same slide at various stages during an example process of slide digitization, in accordance with some embodiments. In the illustrated example, an intra-serial section slides 1504 may include a reference serial section 1508 and a candidate serial section 1512 on the same slide. An “intra- serial section slide,” as used throughout this disclosure is a slide that has more than one serial section placed on it. The rest of the slides as illustrated include serial slides at various points of the method as described above and throughout this disclosure. An intra-serial binary mask section slide is shown and may include a binary mask 1516 of a reference serial section 1520 and a candidate serial section 1524 on the same slide. The repeating serial section 1528 additionally may include a reference serial section 1532 and a candidate serial section 1536. Lastly, the regenerated candidate serial section map 1540 may include a reference serial section 1544 and a candidate serial section 1548.Continuing to reference FIG. 15, in some embodiments, no slide is manually labeled as an intra-seral section slide before the process of slide digitization. Instead, an inline registration process of an intra-serial section slide is performed by a processor with automatic computer programs. In the first phase of the registration of intra-serial section slide, the inline registration program finds all repeating matches of serial sections matching features of the reference template, such as shapes and / or sizes. In the second phase of the registration of intra-serial section slide, the inline registration program expands the reference template to be applied onto all matching serial sections found in the first phase. When completing the first phase and the second phase, the slide being assessed is registered as an intra-serial section slide.82 Atorney Docket No. 1519-174PCT1With further reference to FIG. 15, the information about the count and location of serial sections on an intra-serial section slide may be utilized in the process of digitizing intra-serial section slide. In some embodiments, a user may input parameters and / or instructions regarding the count and location of serial sections through a user interface. In some embodiments, such information may be detected by an inline algorithm configured to identify and / or locate subsections of tissue and / or sub-components of serial sections having similarities. Executing the inline algorithm stored in a memory device, a processor assesses the content of every slide to determine whether there are repeated fragments present. In an embodiment where repeating fragments are present on a slide, they may be identified by the processor as an intra-serial sections slide. An intra-serial sections slide may also be referred to as an intra-serial fragment slide. The intra-serial section slide may be H&E-stained and / or non-H&E-stained.Continuing to reference FIG. 15, during the process of digitizing an intra-serial section slide, one of the serial sections may be identified as a reference serial section. This determination may be determined by the processor by way of locating repeating patterns. Other serial sections on the same slide may then be checked against the reference serial section. In turn, the corresponding tissue maps are corrected in view of the reference serial section. In FIG. 15 specifically, a reference serial section is assessed by the processor to identify and recover the missing tissue fragment from a candidate serial section.Referring now to FIG. 16, an exemplary embodiment of a simplified system for color gamut normalization for digital slides is illustrated. For example, the user may be used to view different biological organizations (e.g., tissue, cells, organelles, bacteria, viruses, etc.) at different magnification levels. In some embodiments, each level may have a corresponding semantic meaning as described below.With continued reference to FIG. 16, the first level 1602, the magnification may be sufficient to view macro structures of tissue. Typically, the size of tissue is over 100 pm. In some embodiments, a 40X magnification may be used to see macro structures of tissue under a user. In some embodiments, the first level 1602 may be associated with a semantic meaning and may be called the tissue level.With continued reference to FIG. 16, at the second level 1604, the magnification may be sufficient to view a single cell. Typically, the size of cells ranges from 1-100 pm. For instance, yeast cells are 3-4 pm in diameter, and human red blood cells are 7-8 pm in diameter. In some83 Atorney Docket No. 1519-174PCT1embodiments, a 400* magnification (assuming 10* eyepiece and 40* objective lens) may be used to see a single cell under a user. In some embodiments, the second level 1604 may be associated with a semantic meaning and may be called the cell level.With continued reference to FIG. 16, at the third level 1606, the magnification may be sufficient to view organelles within cells. Organelles within cells may include the nucleus, mitochondria, lysosomes, the endoplasmic reticulum, and the Golgi apparatus. Typically, the size of organelles ranges from 100 nm-1 pm. In some embodiments, a 640 * magnification may be used to see a mitochondrion under a user. In some embodiments, the third level 1606 may be associated with a semantic meaning and may be called the organelle level.Still referring to FIG. 16, magnification levels 1602 to 1606 illustrates and that many other magnification levels (or ranges of magnification levels) and their corresponding semantic meanings may be possible. In some embodiments, the magnification levels of a digitized user may be continuous over a range of magnification levels (e.g., continuously adjustable from 2* to 500x magnification). In some embodiments, the magnification levels may be selected from a discrete set of magnification levels (e.g., 50*, 400*, 640* magnifications).Referring now to FIG. 17, an exemplary embodiment image of the original object view at a magnification level is illustrated. In some embodiments, the image representation of the object may correspond to the image representation, e.g., as described above. As depicted in FIG. 17, there are two regions 1702 and 1704, and the regions 1702 and 1704 may indicate different tissue types. For example, the region 1702 may indicate the adipose tissue and the region 1702 may indicate tumor tissue. The regions 1702 and 1704 may manifest high variance in perceptual clarity, which may be measured by one or more perceptual characteristics (e.g., brightness). For example, the region 1702 (e.g., the adipose tissue) has a higher brightness than that of the region 1704 due to the difference in thickness.Referring now to FIG. 18, the image representation described in FIG. 17 processed by a color normalization process based on perceptual features and without using autonomous, segment specific transformations as described above. The color normalization process may transform the region 1702 into a transformed region 1802 and may transform the region 1704 into a transformed region 1804 by changing the one or more perceptual characteristics of the WSI. As depicted in FIG. 18, the approach of using global transformation of the whole WSI, rather than the segment-specific transformations described above, is suboptimal because it tends84 Atorney Docket No. 1519-174PCT1to make some regions perceptually clear while making other regions less clear, also it tends to make the entire image representation of the object look similar. For example, compared with the original region 1704, the transformed region 1804 is less perceptually clear. As the other example, compared with the original region 1702, the transformed region 1802 loses its original color gradient by indicating an unnatural pink hue.Still referring to FIG. 18, manual and regional transformation based on perceptual characteristics - which also do not use autonomous, segment-specific transformations as described above- is also less optimal, because diagnostically meaningful interpretation of WSI in digital pathology should be made on semantically similar aggregate based on biological features (e.g., ducts, stroma, adipose tissue) rather than perceptually similar features (e.g., color, saturation, intensity, gamma). Hence there is a desire in digital pathology to view the WSI based on semantically meaningful biological features. For example, the WSI may be segmented based on the semantical meaning associated with the magnification level of user.Referring now to FIG. 19 and 20, the segmented image representation described in FIG. 17 is illustrated, in accordance with certain embodiments of the present disclosure. In contrast to FIG. 18, FIGS. 19 and 20 illustrate the effects of processing an image representation using autonomous, segment specific transformations as described above. Specifically, FIG. 19 illustrates the segmentation of the image representation shown in FIG. 17 based on magnification level, and FIG. 20 illustrates the result of applying a segment-specific transformation to the image representation in FIG. 19. As shown in FIG. 19, the image representation is segmented to match the semantic meaning of the magnification level. In some embodiments, the magnification level may correspond to the first level 1602, which has the semantic meaning of tissue level. The computer vision model, such as the DINO, may be executed to segment the image representation based on the difference in thickness to match the magnification level. Specifically, the computer vision model may segment the image representation into a first segment 1906 and a second segment 1908, which have different thickness, by identifying a first bounding path 1902 and a second bounding path 1904. The first segment 1906 may represent the first region (e.g., the region 1704) and the second segment 1908 may represent the second region (e.g., the region 1702).Referring now to FIG. 20, in some embodiments, a segment-specific transformation model, such as the segment-specific transformation model, is executed to transform the first85 Atorney Docket No. 1519-174PCT1segment 1906 into a first processed segment 2006. Specifically, the segment-specific transformation model may transform the first segment 1906 into a first processed segment 2006 by increasing its brightness. For example, as contrasted with the region 1802 of the image representation processed by the approach in FIG. 18, the region 2002 keeps the same natural color gradient of the region 1702 of the image representation of the original object. Likewise 1908 may be transformed into a second processed segment 2008.Now referring to FIG. 21, a region of the image representation described in FIG. 17, in accordance with certain embodiments of the present disclosure is illustrated. In some embodiments consistent with FIG. 17, a region may correspond to a region 1706 which the user may be interested in, and the user may magnify the user from the first level 1602 to the second level 1604 to view the more detailed structure in the region 1706. In some embodiments, the second level 1604 may have the semantic meaning of cell level and the computer vision model may be executed to segment the image representation to the cell level. As shown in FIG. 21, the cell manifests high variance in one or more perceptual characteristics (e.g., brightness). A segment-specific transformation model is then executed to transform a segment into a processed segment. According to some embodiments, one or more processes of methods as described herein may be repeated until all segments of the plurality of segments are processed into a plurality of processed segments, and the plurality of processed segments may be combined to form a processed image representation.Referring now to FIG. 22, an exemplary embodiment of a machine-learning module 2200 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 2204 to generate an algorithm instantiated in hardware or software logic, data structures, and / or functions that will be performed by a computing device / module to produce outputs 2208 given data provided as inputs 2212; 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. 22, “training data,” as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories86 Atorney Docket No. 1519-174PCT1of data elements. For instance, and without limitation, training data 2204 may include a plurality of data entries, also known as “training examples,” 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 2204 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 2204 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 2204 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 nonlimiting example, training data 2204 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 2204 may be linked to descriptors of categories by tags, tokens, or other data elements; for instance, and without limitation, training data 2204 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. 22, training data 2204 may include one or more elements that are not categorized; that is, training data 2204 may not be formatted or contain descriptors for some elements of data. Machine-learning algorithms and / or other processes may sort training data 2204 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 a87 Atorney Docket No. 1519-174PCT1statistically significant prevalence of n-grams containing such words in a particular order; such 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 machinelearning 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 2204 to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data 2204 used by machine-learning module 2200 may correlate any input data as described in this disclosure to any output data as described in this disclosure.Further referring to FIG. 22, 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 2216. Training data classifier 2216 may include a “classifier,” which as used in this disclosure is a machine-learning model as defined below, such as a data structure representing and / or using 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 2200 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 2204. 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.Still referring to FIG. 22, Computing device may be configured to generate a classifier88 Atorney Docket No. 1519-174PCT1using a Naive Bayes classification algorithm. Naive Bayes classification algorithm generates classifiers by assigning class labels to problem instances, represented as vectors of element values. Class labels are drawn from a finite set. Naive Bayes classification algorithm may include generating a family of algorithms that assume that the value of a particular element is independent of the value of any other element, given a class variable. Naive Bayes classification algorithm may be based on Bayes Theorem expressed as P(A / B)= P(B / A) P(A)^-P(B), where P(A / B) is the probability of hypothesis A given data B also known as posterior probability; P(B / A) is the probability of data B given that the hypothesis A was true; P(A) is the probability of hypothesis A being true regardless of data also known as prior probability of A; and P(B) is the probability of the data regardless of the hypothesis. A naive Bayes algorithm may be generated by first transforming training data into a frequency table. Computing device may then calculate a likelihood table by calculating probabilities of different data entries and classification labels. Computing device may utilize a naive Bayes equation to calculate a posterior probability for each class. A class containing the highest posterior probability is the outcome of prediction. Naive Bayes classification algorithm may include a gaussian model that follows a normal distribution. Naive Bayes classification algorithm may include a multinomial model that is used for discrete counts. Naive Bayes classification algorithm may include a Bernoulli model that may be utilized when vectors are binary.With continued reference to FIG. 22, Computing device may be configured to generate a classifier using a K-nearest neighbors (KNN) algorithm. A “K-nearest neighbors algorithm” as used in this disclosure, includes a classification method that utilizes feature similarity to analyze how closely out-of-sample- features resemble training data to classify input data to one or more clusters and / or categories of features as represented in training data; this may be performed by representing both training data and input data in vector forms, and using one or more measures of vector similarity to identify classifications within training data, and to determine a classification of input data. K-nearest neighbors algorithm may include specifying a K-value, or a number directing the classifier to select the k most similar entries training data to a given sample, determining the most common classifier of the entries in the database, and classifying the known sample; this may be performed recursively and / or iteratively to generate a classifier that may be used to classify input data as further samples. For instance, an initial set of samples may be performed to cover an initial heuristic and / or “first guess” at an output and / or89 Atorney Docket No. 1519-174PCT1relationship, which may be seeded, without limitation, using expert input received according to any process as described herein. As a non-limiting example, an initial heuristic may include a ranking of associations between inputs and elements of training data. Heuristic may include selecting some number of highest-ranking associations and / or training data elements.With continued reference to FIG. 22, generating k-nearest neighbors algorithm may generate a first vector output containing a data entry cluster, generating a second vector output containing an input data, and calculate the distance between the first vector output and the second vector output using any suitable norm such as cosine similarity, Euclidean distance measurement, or the like. Each vector output may be represented, without limitation, as an n- tuple of values, where n is at least two values. Each value of n-tuple of values may represent a measurement or other quantitative value associated with a given category of data, or attribute, examples of which are provided in further detail below; a vector may be represented, without limitation, in n-dimensional space using an axis per category of value represented in n-tuple of values, such that a vector has a geometric direction characterizing the relative quantities of attributes in the n-tuple as compared to each other. Two vectors may be considered equivalent where their directions, and / or the relative quantities of values within each vector as compared to each other, are the same; thus, as a non-limiting example, a vector represented as [5, 10, 15] may be treated as equivalent, for purposes of this disclosure, as a vector represented as [1, 2, 3], Vectors may be more similar where their directions are more similar, and more different where their directions are more divergent; however, vector similarity may alternatively or additionally be determined using averages of similarities between like attributes, or any other measure of similarity suitable for any n-tuple of values, or aggregation of numerical similarity measures for the purposes of loss functions as described in further detail below. Any vectors as described herein may be scaled, such that each vector represents each attribute along an equivalent scale of values. Each vector may be “normalized,” or divided by a “length” attribute, such as a length attribute I as derived using a Pythagorean norm: I = SF=oai2where a, is attribute number i of the vector. Scaling and / or normalization may function to make vector comparison independent of absolute quantities of attributes, while preserving any dependency on similarity of attributes; this may, for instance, be advantageous where cases represented in training data are represented by different quantities of samples, which may result in proportionally equivalent vectors with divergent values.90 Atorney Docket No. 1519-174PCT1With further reference to FIG. 22, training examples for use as training data may be selected from a population of potential examples according to cohorts relevant to an analytical problem to be solved, a classification task, or the like. Alternatively or additionally, training data may be selected to span a set of likely circumstances or inputs for a machine-learning model and / or process to encounter when deployed. For instance, and without limitation, for each category of input data to a machine-learning process or model that may exist in a range of values in a population of phenomena such as images, user data, process data, physical data, or the like, a computing device, processor, and / or machine-learning model may select training examples representing each possible value on such a range and / or a representative sample of values on such a range. Selection of a representative sample may include selection of training examples in proportions matching a statistically determined and / or predicted distribution of such values according to relative frequency, such that, for instance, values encountered more frequently in a population of data so analyzed are represented by more training examples than values that are encountered less frequently. Alternatively or additionally, a set of training examples may be compared to a collection of representative values in a database and / or presented to a user, so that a process can detect, automatically or via user input, one or more values that are not included in the set of training examples. Computing device, processor, and / or module may automatically generate a missing training example; this may be done by receiving and / or retrieving a missing input and / or output value and correlating the missing input and / or output value with a corresponding output and / or input value collocated in a data record with the retrieved value, provided by a user and / or other device, or the like.Continuing to refer to FIG. 22, computer, processor, and / or module may be configured to preprocess training data. “Preprocessing” training data, as used in this disclosure, is transforming training data from raw form to a format that can be used for training a machine learning model. Preprocessing may include sanitizing, feature selection, feature scaling, data augmentation and the like.Still referring to FIG. 22, computer, processor, and / or module may be configured to sanitize training data. “Sanitizing” training data, as used in this disclosure, is a process whereby training examples are removed that interfere with convergence of a machine-learning model and / or process to a useful result. For instance, and without limitation, a training example may include an input and / or output value that is an outlier from typically encountered values, such91 Atorney Docket No. 1519-174PCT1that a machine-learning algorithm using the training example will be adapted to an unlikely amount as an input and / or output; a value that is more than a threshold number of standard deviations away from an average, mean, or expected value, for instance, may be eliminated. Alternatively or additionally, one or more training examples may be identified as having poor quality data, where “poor quality” is defined as having a signal to noise ratio below a threshold value. Sanitizing may include steps such as removing duplicative or otherwise redundant data, interpolating missing data, correcting data errors, standardizing data, identifying outliers, and the like. In a nonlimiting example, sanitization may include utilizing algorithms for identifying duplicate entries or spell-check algorithms.As a non-limiting example, and with further reference to FIG. 22, images used to train an image classifier or other machine-learning model and / or process that takes images as inputs or generates images as outputs may be rejected if image quality is below a threshold value. For instance, and without limitation, computing device, processor, and / or module may perform blur detection, and eliminate one or more Blur detection may be performed, as a non-limiting example, by taking Fourier transform, or an approximation such as a Fast Fourier Transform (FFT) of the image and analyzing a distribution of low and high frequencies in the resulting frequency-domain depiction of the image; numbers of high-frequency values below a threshold level may indicate blurriness. As a further non-limiting example, detection of blurriness may be performed by convolving an image, a channel of an image, or the like with a Laplacian kernel; this may generate a numerical score reflecting a number of rapid changes in intensity shown in the image, such that a high score indicates clarity and a low score indicates blurriness. Blurriness detection may be performed using a gradient-based operator, which measures operators based on the gradient or first derivative of an image, based on the hypothesis that rapid changes indicate sharp edges in the image, and thus are indicative of a lower degree of blurriness. 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. 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. Blur detection may be performed by using discrete cosine transform (DCT) coefficients in order to compute a focus level of an image from its frequency content.Continuing to refer to FIG. 22, computing device, processor, and / or module may be92 Atorney Docket No. 1519-174PCT1configured to precondition one or more training examples. For instance, and without limitation, where a machine learning model and / or process has one or more inputs and / or outputs requiring, transmitting, or receiving a certain number of bits, samples, or other units of data, one or more training examples’ elements to be used as or compared to inputs and / or outputs may be modified to have such a number of units of data. For instance, a computing device, processor, and / or module may convert a smaller number of units, such as in a low pixel count image, into a desired number of units, for instance by upsampling and interpolating. As a non-limiting example, a low pixel count image may have 100 pixels, however a desired number of pixels may be 128. Processor may interpolate the low pixel count image to convert the 100 pixels into 128 pixels. It should also be noted that one of ordinary skill in the art, upon reading this disclosure, would know the various methods to interpolate a smaller number of data units such as samples, pixels, bits, or the like to a desired number of such units. In some instances, a set of interpolation rules may be trained by sets of highly detailed inputs and / or outputs and corresponding inputs and / or outputs downsampled to smaller numbers of units, and a neural network or other machine learning model that is trained to predict interpolated pixel values using the training data. As a non-limiting example, a sample input and / or output, such as a sample picture, with sample- expanded data units (e.g., pixels added between the original pixels) may be input to a neural network or machine-learning model and output a pseudo replica sample-picture with dummy values assigned to pixels between the original pixels based on a set of interpolation rules. As a non-limiting example, in the context of an image classifier, a machine-learning model may have a set of interpolation rules trained by sets of highly detailed images and images that have been downsampled to smaller numbers of pixels, and a neural network or other machine learning model that is trained using those examples to predict interpolated pixel values in a facial picture context. As a result, an input with sample-expanded data units (the ones added between the original data units, with dummy values) may be run through a trained neural network and / or model, which may fill in values to replace the dummy values. Alternatively or additionally, processor, computing device, and / or module may utilize sample expander methods, a low-pass filter, or both. As used in this disclosure, a “low-pass filter” is a filter that passes signals with a frequency lower than a selected cutoff frequency and attenuates signals with frequencies higher than the cutoff frequency. The exact frequency response of the filter depends on the filter design. Computing device, processor, and / or module may use averaging, such as luma or chroma93 Atorney Docket No. 1519-174PCT1averaging in images, to fill in data units in between original data units.In some embodiments, and with continued reference to FIG. 22, computing device, processor, and / or module may down-sample elements of a training example to a desired lower number of data elements. As a non-limiting example, a high pixel count image may have 256 pixels, however a desired number of pixels may be 128. Processor may down-sample the high pixel count image to convert the 256 pixels into 128 pixels. In some embodiments, processor may be configured to perform downsampling on data. Downsampling, also known as decimation, may include removing every Nth entry in a sequence of samples, all but every Nth entry, or the like, which is a process known as “compression,” and may be performed, for instance by an N-sample compressor implemented using hardware or software. Anti-aliasing and / or anti-imaging filters, and / or low-pass filters, may be used to clean up side-effects of compression.Further referring to FIG. 22, feature selection includes narrowing and / or filtering training data to exclude features and / or elements, or training data including such elements, that are not relevant to a purpose for which a trained machine-learning model and / or algorithm is being trained, and / or collection of features and / or elements, or training data including such elements, on the basis of relevance or utility for an intended task or purpose for a trained machine-learning model and / or algorithm is being trained. Feature selection may be implemented, without limitation, using any process described in this disclosure, including without limitation using training data classifiers, exclusion of outliers, or the like.With continued reference to FIG. 22, feature scaling may include, without limitation, normalization of data entries, which may be accomplished by dividing numerical fields by norms thereof, for instance as performed for vector normalization. Feature scaling may include absolute maximum scaling, wherein each quantitative datum is divided by the maximum absolute value of all quantitative data of a set or subset of quantitative data. Feature scaling may include min-max scaling, in which each value X has a minimum value Xminin a set or subset of values subtracted therefrom, with the result divided by the range of the values, give maximum value in the set or subset XmaxXnew= -mm. Feature scaling may include mean normalization, which^max~ min involves use of a mean value of a set and / or subset of values, Xmeanwith maximum and — minimum values: Xnew= -mean. Feature scaling may include standardization, where a^max~^min94 Atorney Docket No. 1519-174PCT1difference between X and Xmeanis divided by a standard deviation a of a set or subset of values: Xnew= —^ Scaling may be performed using a median value of a set or subset Xmedianand / or interquartile range (IQR), which represents the difference between the 25thpercentile value and the 50111percentile value (or closest values thereto by a rounding protocol), such as: Xnew=Persons skilled in the art, upon reviewing the entirety of this disclosure, will IQR be aware of various alternative or additional approaches that may be used for feature scaling.Further referring to FIG. 22, computing device, processor, and / or module may be configured to perform one or more processes of data augmentation. “Data augmentation” as used in this disclosure is addition of data to a training set using elements and / or entries already in the dataset. Data augmentation may be accomplished, without limitation, using interpolation, generation of modified copies of existing entries and / or examples, and / or one or more generative Al processes, for instance using deep neural networks and / or generative adversarial networks; generative processes may be referred to alternatively in this context as “data synthesis” and as creating “synthetic data.” Augmentation may include performing one or more transformations on data, such as geometric, color space, affine, brightness, cropping, and / or contrast transformations of images.Still referring to FIG. 22, machine-learning module 2200 may be configured to perform a lazy-learning process 2220 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 2204. Heuristic may include selecting some number of highest-ranking associations and / or training data 2204 elements. Lazy learning may implement any suitable lazy learning algorithm, including without limitation a K-nearest neighbors algorithm, a lazy naive 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 as95 Atorney Docket No. 1519-174PCT1described in further detail below.Alternatively or additionally, and with continued reference to FIG. 22, machine-learning processes as described in this disclosure may be used to generate machine-learning models 2224. A “machine-learning model,” as used in this disclosure, is a data structure representing and / or instantiating 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 2224 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 2224 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 2204 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 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. 22, machine-learning algorithms may include at least a supervised machine-learning process 2228. At least a supervised machine-learning process 2228, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to generate one or more data structures representing and / or instantiating 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 input as described above or through incorporation as inputs, outputs as described above or through incorporation 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.96 Atorney Docket No. 1519-174PCT1Scoring 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 2204. 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 2228 that may be used to determine relation between inputs and outputs. Supervised machine-learning processes may include classification algorithms as defined above.With further reference to FIG. 22, training a supervised machine-learning process may include, without limitation, iteratively updating coefficients, biases, weights based on an error function, expected loss, and / or risk function. For instance, an output generated by a supervised machine-learning model using an input example in a training example may be compared to an output example from the training example; an error function may be generated based on the comparison, which may include any error function suitable for use with any machine-learning algorithm described in this disclosure, including a square of a difference between one or more sets of compared values or the like. Such an error function may be used in turn to update one or more weights, biases, coefficients, or other parameters of a machine-learning model through any suitable process including without limitation gradient descent processes, least-squares processes, and / or other processes described in this disclosure. This may be done iteratively and / or recursively to gradually tune such weights, biases, coefficients, or other parameters. Updating may be performed, in neural networks, using one or more back-propagation algorithms. Iterative and / or recursive updates to weights, biases, coefficients, or other parameters as described above may be performed until currently available training data is exhausted and / or until a convergence test is passed, where a “convergence test” is a test for a condition selected as indicating that a model and / or weights, biases, coefficients, or other parameters thereof has reached a degree of accuracy. A convergence test may, for instance, compare a difference between two or more successive errors or error function values, where differences below a threshold amount may be taken to indicate convergence. Alternatively or additionally, one or more errors and / or error function values evaluated in training iterations may be compared to a threshold.Still referring to FIG. 22, a computing device, processor, and / or module may be configured to perform method, method step, sequence of method steps and / or algorithm97 Atorney Docket No. 1519-174PCT1described in reference to this figure, in any order and with any degree of repetition. For instance, a computing device, processor, and / or module may be configured to perform a single step, sequence and / or algorithm 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. A computing device, processor, and / or module may perform any step, sequence of steps, or algorithm in parallel, such 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.Further referring to FIG. 22, machine learning processes may include at least an unsupervised machine-learning processes 2232. 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 2232 may not require a response variable; unsupervised processes 2232may 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. 22, machine-learning module 2200 may be designed and configured to create a machine-learning model 2224 using techniques for development of linear regression models. Linear regression models may include ordinary least squares regression, which aims to 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 may98 Atorney Docket No. 1519-174PCT1include 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. 22, 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 include cross-decomposition algorithms, including partial least squares and / or canonical correlation analysis. Machine-learning algorithms may include naive Bayes methods. Machinelearning 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 net99 Atorney Docket No. 1519-174PCT1algorithms, including convolutional neural net processes.Still referring to FIG. 22, a machine-learning model and / or process may be deployed or instantiated by incorporation into a program, apparatus, system and / or module. For instance, and without limitation, a machine-learning model, neural network, and / or some or all parameters thereof may be stored and / or deployed in any memory or circuitry. Parameters such as coefficients, weights, and / or biases may be stored as circuit-based constants, such as arrays of wires and / or binary inputs and / or outputs set at logic “1” and “0” voltage levels in a logic circuit to represent a number according to any suitable encoding system including twos complement or the like or may be stored in any volatile and / or non-volatile memory. Similarly, mathematical operations and input and / or output of data to or from models, neural network layers, or the like may be instantiated in hardware circuitry and / or in the form of instructions in firmware, machine-code such as binary operation code instructions, assembly language, or any higher- order programming language. Any technology for hardware and / or software instantiation of memory, instructions, data structures, and / or algorithms may be used to instantiate a machinelearning process and / or model, including without limitation any combination of production and / or configuration of non-reconfigurable hardware elements, circuits, and / or modules such as without limitation ASICs, production and / or configuration of reconfigurable hardware elements, circuits, and / or modules such as without limitation FPGAs, production and / or of non- reconfigurable and / or configuration non-rewritable memory elements, circuits, and / or modules such as without limitation non-rewritable ROM, production and / or configuration of reconfigurable and / or rewritable memory elements, circuits, and / or modules such as without limitation rewritable ROM or other memory technology described in this disclosure, and / or production and / or configuration of any computing device and / or component thereof as described in this disclosure. Such deployed and / or instantiated machine-learning model and / or algorithm may receive inputs from any other process, module, and / or component described in this disclosure, and produce outputs to any other process, module, and / or component described in this disclosure.Continuing to refer to FIG. 22, any process of training, retraining, deployment, and / or instantiation of any machine-learning model and / or algorithm may be performed and / or repeated after an initial deployment and / or instantiation to correct, refine, and / or improve the machinelearning model and / or algorithm. Such retraining, deployment, and / or instantiation may be100 Atorney Docket No. 1519-174PCT1performed as a periodic or regular process, such as retraining, deployment, and / or instantiation at regular elapsed time periods, after some measure of volume such as a number of bytes or other measures of data processed, a number of uses or performances of processes described in this disclosure, or the like, and / or according to a software, firmware, or other update schedule. Alternatively or additionally, retraining, deployment, and / or instantiation may be event-based, and may be triggered, without limitation, by user inputs indicating sub-optimal or otherwise problematic performance and / or by automated field testing and / or auditing processes, which may compare outputs of machine-learning models and / or algorithms, and / or errors and / or error functions thereof, to any thresholds, convergence tests, or the like, and / or may compare outputs of processes described herein to similar thresholds, convergence tests or the like. Event-based retraining, deployment, and / or instantiation may alternatively or additionally be triggered by receipt and / or generation of one or more new training examples; a number of new training examples may be compared to a preconfigured threshold, where exceeding the preconfigured threshold may trigger retraining, deployment, and / or instantiation.Still referring to FIG. 22, retraining and / or additional training may be performed using any process for training described above, using any currently or previously deployed version of a machine-learning model and / or algorithm as a starting point. Training data for retraining may be collected, preconditioned, sorted, classified, sanitized or otherwise processed according to any process described in this disclosure. Training data may include, without limitation, training examples including inputs and correlated outputs used, received, and / or generated from any version of any system, module, machine-learning model or algorithm, apparatus, and / or method described in this disclosure; such examples may be modified and / or labeled according to user feedback or other processes to indicate desired results, and / or may have actual or measured results from a process being modeled and / or predicted by system, module, machine-learning model or algorithm, apparatus, and / or method as “desired” results to be compared to outputs for training processes as described above.Redeployment may be performed using any reconfiguring and / or rewriting of reconfigurable and / or rewritable circuit and / or memory elements; alternatively, redeployment may be performed by production of new hardware and / or software components, circuits, instructions, or the like, which may be added to and / or may replace existing hardware and / or software components, circuits, instructions, or the like.101 Atorney Docket No. 1519-174PCT1Further referring to FIG. 22, one or more processes or algorithms described above may be performed by at least a dedicated hardware unit 2236. 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 2236 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 2236 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 2236 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. 23, an exemplary embodiment of neural network 2300 is illustrated. A neural network 2300 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 convolutional neural network, including an input layer of nodes 2304, one or more intermediate layers 2308, and an output layer of nodes 2312. 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 the102 Atorney Docket No. 1519-174PCT1connections 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. 24, an exemplary embodiment of a node 2400 of a neural network is illustrated. A node may include, without limitation, a plurality of inputs x, that 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 (x) = givenex_e-x input x, a tanh (hyperbolic tangent) function, of the formgX+e^x, a tanh derivative function such as / (x) = tanh2(x), a rectified linear unit function such as (x) = max (0, x), a “leaky” and / or “parametric” rectified linear unit function such as (x) = max (ax, x) for some a, an exponential linear units function such as (x) =<Q forsomevalue of a(this function may be replaced and / or weighted by its own derivative in some embodiments), asoftmax function such as f(X[) = where the inputs to an instant layer are x;, a swishfunction such as (x) = x * sigmoid(x), a Gaussian error linear unit function such as f(x) = a(l + tanhfor some values of a, b, and r, and / or a scaled exponential linear unit function such as . Fundamentally, there is no limit to the103 Atorney Docket No. 1519-174PCT1nature of functions of inputs xt that may be used as activation functions. As a non-limiting and illustrative example, node may perform a weighted sum of inputs using weights w> that are multiplied by respective inputs xt. 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 (p, which may generate one or more outputs y. Weight wt applied to an input Xi may indicate whether the input is “excitatory,” indicating that it has strong influence on the one or more outputs^, 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 w, may be determined by training a neural network using training data, which may be performed using any suitable process as described above.Now referring to FIG. 25, an exemplary embodiment of fuzzy set comparison 2500 is illustrated. In a non-limiting embodiment, the fuzzy set comparison. In a non-limiting embodiment, fuzzy set comparison 2500 may be consistent with fuzzy set comparison in FIG. 1. In another non-limiting the fuzzy set comparison 2500 may be consistent with the name / version matching as described herein. For example and without limitation, the parameters, weights, and / or coefficients of the membership functions may be tuned using any machine-learning methods for the name / version matching as described herein. In another non-limiting embodiment, the fuzzy set may represent a scanned label and historically scanned label from FIG. 1.Alternatively or additionally, and still referring to FIG. 25, fuzzy set comparison 2500 may be generated as a function of determining data compatibility threshold. The compatibility threshold may be determined by a computing device. In some embodiments, a computing device may use a logic comparison program, such as, but not limited to, a fuzzy logic model to determine the compatibility threshold and / or version authenticator. Each such compatibility threshold may be represented as a value for a posting variable representing the compatibility threshold, or in other words a fuzzy set as described above that corresponds to a degree of compatibility and / or allowability as calculated using any statistical, machine-learning, or other method that may occur to a person skilled in the art upon reviewing the entirety of this disclosure. In some embodiments, determining the compatibility threshold and / or version104 Atorney Docket No. 1519-174PCT1authenticator may include using a linear regression model. A linear regression model may include a machine learning model. A linear regression model may map statistics such as, but not limited to, frequency of the same range of version numbers, and the like, to the compatibility threshold and / or version authenticator. In some embodiments, determining the compatibility threshold of any posting may include using a classification model. A classification model may be configured to input collected data and cluster data to a centroid based on, but not limited to, frequency of appearance of the range of versioning numbers, linguistic indicators of compatibility and / or allowability, and the like. Centroids may include scores assigned to them such that the compatibility threshold may each be assigned a score. In some embodiments, a classification model may include a K-means clustering model. In some embodiments, a classification model may include a particle swarm optimization model. In some embodiments, determining a compatibility threshold may include using a fuzzy inference engine. A fuzzy inference engine may be configured to map one or more compatibility threshold using fuzzy logic. In some embodiments, a plurality of computing devices may be arranged by a logic comparison program into compatibility arrangements. A “compatibility arrangement” as used in this disclosure is any grouping of objects and / or data based on skill level and / or output score. Membership function coefficients and / or constants as described above may be tuned according to classification and / or clustering algorithms. For instance, and without limitation, a clustering algorithm may determine a Gaussian or other distribution of questions about a centroid corresponding to a given compatibility threshold and / or version authenticator, and an iterative or other method may be used to find a membership function, for any membership function type as described above, that minimizes an average error from the statistically determined distribution, such that, for instance, a triangular or Gaussian membership function about a centroid representing a center of the distribution that most closely matches the distribution. Error functions to be minimized, and / or methods of minimization, may be performed without limitation according to any error function and / or error function minimization process and / or method as described in this disclosure.Still referring to FIG. 25, inference engine may be implemented according to input a plurality of scanned labels and a plurality of historically scanned labels. For instance, an acceptance variable may represent a first measurable value pertaining to the classification of a plurality of scanned label to an historically scanned label. Continuing the example, an output105 Atorney Docket No. 1519-174PCT1variable may represent a confidence score. In an embodiment, a plurality of scanned label and / or an historically scanned label may be represented by their own fuzzy set. In other embodiments, an evaluation factor may be represented as a function of the intersection two fuzzy sets as shown in FIG. 25, An inference engine may combine rules, such as any semantic versioning, semantic language, version ranges, and the like thereof. The degree to which a given input function membership matches a given rule may be determined by a triangular norm or “T-norm” of the rule or output function with the input function, such as min (a, b), product of a and b, drastic product of a and b, Hamacher product of a and b, or the like, satisfying the rules of commutativity (T(a, b) = T(b, a)), monotonicity: (T(a, b) < T(c, d) if a < c and b < d), (associativity: T(a, T(b, c)) = T(T(a, b), c)), and the requirement that the number 1 acts as an identity element. Combinations of rules (“and” or “or” combination of rule membership determinations) may be performed using any T-conorm, as represented by an inverted T symbol or “1,” such as max(a, b), probabilistic sum of a and b (a+b-a*b), bounded sum, and / or drastic T- conorm; any T-conorm may be used that satisfies the properties of commutativity: l(a, b) = l(b, a), monotonicity: l(a, b) < l(c, d) if a < c and b < d, associativity: l(a, l(b, c)) = l(l(a, b), c), and identity element of 0. Alternatively or additionally T-conorm may be approximated by sum, as in a “product-sum” inference engine in which T-norm is product and T-conorm is sum. A final output score or other fuzzy inference output may be determined from an output membership function as described above using any suitable defuzzification process, including without limitation Mean of Max defuzzification, Centroid of Area / Center of Gravity defuzzification, Center Average defuzzification, Bisector of Area defuzzification, or the like. Alternatively or additionally, output rules may be replaced with functions according to the Takagi-Sugeno-King (TSK) fuzzy model.

[0002] A first fuzzy set 2504 may be represented, without limitation, according to a first membership function 2508 representing a probability that an input falling on a first range of values 2512 is a member of the first fuzzy set 2504, where the first membership function 2508 has values on a range of probabilities such as without limitation the interval [0,1], and an area beneath the first membership function 2508 may represent a set of values within first fuzzy set 2504. Although first range of values 2512 is illustrated for clarity in this exemplary depiction as a range on a single number line or axis, first range of values 2512 may be defined on two or more dimensions, representing, for instance, a Cartesian product between a plurality of ranges,106 Atorney Docket No. 1519-174PCT1curves, axes, spaces, dimensions, or the like. First membership function 2508 may include any suitable function mapping first range of values 2512 to a probability interval, including without limitation a triangular function defined by two linear elements such as line segments or planes that intersect at or below the top of the probability interval. As a non-limiting example, triangular membership function may be defined as:a trapezoidal membership function may be defined as:a sigmoidal function may be defined as:a Gaussian membership function may be defined as:and a bell membership function may be defined as:Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various alternative or additional membership functions that may be used consistently with this disclosure.First fuzzy set 2504 may represent any value or combination of values as described above, including any a plurality of scanned label and historically scanned label. A second fuzzy set 2516, which may represent any value which may be represented by first fuzzy set 2504, may be defined by a second membership function 2520 on a second range 2524; second range 2524 may be identical and / or overlap with first range of values 2512 and / or may be combined with first range via Cartesian product or the like to generate a mapping permitting evaluation overlap of first fuzzy set 2504 and second fuzzy set 2516. Where first fuzzy set 2504 and second fuzzy set 2516 have a region 2536 that overlaps, first membership function 2508 and second membership function 2520 may intersect at a point 2532 representin...

Claims

1. What is claimed is:

1. A system for augmented visualization using activity windows, wherein the system comprises: at least a processor; a memory communicatively connected to the at least a processor, wherein the memory stores instructions configuring the processor to: receive image data from an imaging device; execute at least a first algorithm on the image data, wherein the first algorithm is configured to output annotation data associated with the image data; and generate a display data structure, wherein the display data structure includes at least: a primary window; and an activity window; and an interactive display device, wherein the interactive display device is configured to display to a user, the generated display data structure.

2. The system of claim 1, wherein the imaging device comprises an optical scanner.

3. The system of claim 1, wherein the at least a first algorithm comprises one or more of: an algorithm configured to calculate a fitness measure of the image data and flag the image data accordingly; an algorithm configured to determine a quality metric for the image data; an algorithm configured to identify different cell groups in one or more image data; and an algorithm configured to generate a color gamut correction for one or more image data.

4. The system of claim 1, wherein an activity window further comprises adaptive overlays with metadata at different levels of magnification.

5. The system of claim 4, wherein adaptive overlays further comprises: transparent masks with overlay information at high magnification; contours with overlay information at intermediate magnification; and dots of various sizes with information at lower magnification.

6. The system of claim 1, wherein a user may toggle between annotation data displayed on the activity window at the interactive display device.

7. The system of claim 1, wherein the instructions further configure the processor to:114 Atorney Docket No. 1519-174PCT1accept user input, using interactive display device, selecting a region of interest of image data; display, using the primary window, the selected region of interest of image data; and enable, using interactive display device, zoom and pan over displayed region of interest.

8. The system of claim 1, wherein the instructions further configure the processor to: accept user input, using image segmentation tools, multiple segments of interest from display data structure; composite a virtual composite image from selected segments of interest; and display the virtual composite image.

9. The system of claim 1, wherein the activity window and the primary window present adjacent image data, wherein the adjacent image data comprises altered image data.

10. The system of claim 9, wherein the adjacent image data further comprises scanned tissue slides, and the altered image data further comprises scanned tissue slides with different stains.

11. A method for augmented visualization using activity windows, wherein the method comprises: receiving image data from an imaging device; executing at least a first algorithm on the image data, wherein the first algorithm is configured to output annotation data associated with the image data; and generating a display data structure, wherein the display data structure includes at least: a primary window; and an activity window; and displaying, at interactive display device, the display data structure.

12. The method of claim 11, wherein the imaging device comprises an optical scanner.

13. The method of claim 11, wherein the at least a first algorithm comprises one or more of: an algorithm configured to calculate a fitness measure of the image data and flag the image data accordingly; an algorithm configured to determine a quality metric for the image data; an algorithm configured to identify different cell groups in one or more image data; and an algorithm configured to generate a color gamut correction for one or more image data.

14. The method of claim 11, wherein an activity window further comprises adaptive overlays115 Atorney Docket No. 1519-174PCT1with metadata at different levels of magnification.

15. The method of claim 14, wherein adaptive overlays further comprises: transparent masks with overlay information at high magnification; contours with overlay information at intermediate magnification; and dots of various sizes with information at lower magnification.

16. The method of claim 11, wherein a user may toggle between annotation data displayed on the activity window at the interactive display device.

17. The method of claim 11, wherein the method further comprises: accepting user input, using interactive display device, selecting a region of interest of image data; displaying, using the primary window, the selected region of interest of image data; and enabling, using interactive display device, zoom and pan over displayed region of interest.

18. The method of claim 11, wherein the method further comprises: accepting user input, using image segmentation tools, multiple segments of interest from display data structure; compositing a virtual composite image from selected segments of interest; and displaying the virtual composite image.

19. The method of claim 11, wherein the activity window and the primary window present adjacent image data, wherein the adjacent image data comprises altered image data.

20. The method of claim 19, wherein the adjacent image data further comprises scanned tissue slides, and the altered image data further comprises scanned tissue slides with different stains.

21. A system for augmented visualization using activity windows, wherein the system comprises: at least a processor; a memory communicatively connected to the at least a processor, wherein the memory stores instructions configuring the processor to: receive image data from an imaging device; execute at least a first algorithm on the image data comprising an algorithm configured to determine a quality metric from the image data by116 Atorney Docket No. 1519-174PCT1implementing a process to correct image data using historical image data and to identify different cell groups in the image data, wherein the first algorithm is configured to output annotation data associated with the image data; and generate a display data structure, wherein the display data structure includes at least: a primary window; and an activity window to visualize the image data with overlay of metadata comprising annotation data; and an interactive display device, wherein the interactive display device is configured to display to a user, the generated display data structure.

22. The system of claim 21, wherein the imaging device comprises an optical scanner.

23. The system of claim 21, wherein the at least a first algorithm comprises one or more of: an algorithm configured to calculate a fitness measure of the image data and flag the image data accordingly; and an algorithm configured to generate a color gamut correction for one or more image data.

24. The system of claim 21, wherein an activity window further comprises adaptive overlays with metadata at different levels of magnification.

25. The system of claim 24, wherein adaptive overlays further comprises: transparent masks with overlay information at high magnification; contours with overlay information at intermediate magnification; and dots of various sizes with information at lower magnification.

26. The system of claim 21, wherein a user may toggle between annotation data displayed on the activity window at the interactive display device.

27. The system of claim 21, wherein the instructions further configure the processor to: accept user input, using interactive display device, selecting a region of interest of image data; display, using the primary window, the selected region of interest of image data; and enable, using interactive display device, zoom and pan over displayed region of interest.

28. The system of claim 21, wherein the instructions further configure the processor to: accept user input, using image segmentation tools, multiple segments of interest from117 Atorney Docket No. 1519-174PCT1display data structure; composite a virtual composite image from selected segments of interest; and display the virtual composite image.

29. The system of claim 21, wherein the activity window and the primary window present adjacent image data, wherein the adjacent image data comprises altered image data.

30. The system of claim 29, wherein the adjacent image data further comprises scanned tissue slides, and the altered image data further comprises scanned tissue slides with different stains.

31. A method for augmented visualization using activity windows, wherein the method comprises: receiving image data from an imaging device; executing at least a first algorithm on the image data comprising an algorithm configured to determine a quality metric from the image data by implementing a process to correct image data using historical image data and to identify different cell groups in the image data, wherein the first algorithm is configured to output annotation data associated with the image data; generating a display data structure, wherein the display data structure includes at least: a primary window; and an activity window to visualize the image data with overlay of metadata comprising annotation data; and displaying, at interactive display device, the display data structure.

32. The method of claim 31, wherein the imaging device comprises an optical scanner.

33. The method of claim 31, wherein the at least a first algorithm comprises one or more of: an algorithm configured to calculate a fitness measure of the image data and flag the image data accordingly; and an algorithm configured to generate a color gamut correction for one or more image data.

34. The method of claim 31, wherein an activity window further comprises adaptive overlays with metadata at different levels of magnification.

35. The method of claim 34, wherein adaptive overlays further comprises: transparent masks with overlay information at high magnification; contours with overlay information at intermediate magnification; and118 Atorney Docket No. 1519-174PCT1dots of various sizes with information at lower magnification.

36. The method of claim 31, wherein a user may toggle between annotation data displayed on the activity window at the interactive display device.

37. The method of claim 31, wherein the method further comprises: accepting user input, using interactive display device, selecting a region of interest of image data; displaying, using the primary window, the selected region of interest of image data; and enabling, using interactive display device, zoom and pan over displayed region of interest.

38. The method of claim 31, wherein the method further comprises: accepting user input, using image segmentation tools, multiple segments of interest from display data structure; compositing a virtual composite image from selected segments of interest; and displaying the virtual composite image.

39. The method of claim 31, wherein the activity window and the primary window present adjacent image data, wherein the adjacent image data comprises altered image data.

40. The method of claim 39, wherein the adjacent image data further comprises scanned tissue slides, and the altered image data further comprises scanned tissue slides with different stains.

41. A system for augmented visualization using activity windows, wherein the system comprises: at least a processor; a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive image data from an imaging device; execute one or more algorithms from a suite of algorithms, wherein: a first algorithm comprises an algorithm configured to calculate a fitness measure of the image data, wherein calculating a fitness measure of the image data comprises: determining a confidence score associated with a scanned label as a function of a comparison between the scanned label and a119 Atorney Docket No. 1519-174PCT1plurality of historical scanned labels; flagging the image data as a function of the confidence score; and capturing additional image data as a function of flagging the image data; and a second algorithm comprises an algorithm configured to output annotation data associated with the image data; and generate a display data structure, wherein the display data structure comprises at least: a primary window; and an activity window configured to visualize the image data with an overlay of metadata, wherein the metadata comprises annotation data; and an interactive display device, wherein the interactive display device is configured to display to a user, the display data structure.

42. The system of claim 41, wherein determining a confidence score comprises generating the confidence score using a confidence machine-learning model, wherein the confidence machine-learning model is trained using a confidence training data set.

43. The system of claim 41, wherein capturing additional image data as a function of flagging the image data further comprises: capturing, at the imaging device, in rapid succession a series of images of a subject matter at varying camera lens focal lengths and exposure times; and identifying, by the at least a processor, an image of the series of images of a subject matter with a highest focus quality for a selected region of interest.

44. The system of claim 41, wherein the second algorithm comprises one or more of: an algorithm configured to determine a quality metric for the image data; an algorithm configured to identify different cell groups in one or more image data; and an algorithm configured to generate a color gamut correction for one or more image data.

45. The system of claim 41, wherein the activity window and the primary window present adjacent image data, wherein: the activity window displays altered image data; and the primary window displays the image data.

46. The system of claim 41, wherein the at least a processor is further configured to:120 Atorney Docket No. 1519-174PCT1segment the image data using a segmentation tool; display the segmented image data at the activity window; receive a user input, wherein the user input comprises a selection of one or more segments of the segmented image data; and display the selection of one or more segments of the segmented image data at the primary window.

47. The system of claim 46, wherein: the activity window comprises: a first activity window, wherein the first activity window comprises a first segmented image datum; and a second activity window, wherein the second activity window comprises a second segmented image datum; and the at least a processor is further configured to: receive a user input, wherein the user input comprises a selection of one or more segments of the first segmented image datum and a selection of one or more of the segments of the second segmented image datum; and display, at the primary window, the selection of one or more segments of the first segmented image datum and the selection of the one or more segments of the second segmented image datum.

48. The system of claim 41, wherein the at least a processor is further configured to: receive a user input, wherein the user input comprises a selected region of interest, wherein the selected region of interest is highlighted as a function of a drag-and- drop operation using a mouse, wherein: the user initiates the selection by clicking on a starting point within the activity window; and drags a cursor to an endpoint, thereby defining the region of interest; and display the region of interest at the primary window.

49. The system of claim 48, wherein the at least a processor is further configured to: receive a user input, wherein the user input comprises a selection of a portion of the image data within the activity window to perform an image processing operation as a function of the second algorithm;121 Atorney Docket No. 1519-174PCT1apply the second algorithm to the image data; and display an enhanced image at the primary window.

50. The system of claim 41, wherein the at least a processor enables a user, using the interactive display device, to zoom and pan over the display data structure.

51. A method for augmented visualization using activity windows, wherein the method comprises: receiving, by at least a processor, image data from an imaging device; executing one or more algorithms from a suite of algorithms, wherein: a first algorithm comprises an algorithm configured to calculate a fitness measure of the image data, wherein calculating a fitness measure of the image data comprises: determining a confidence score associated with a scanned label as a function of a comparison between the scanned label and a plurality of historical scanned labels; flagging the image data as a function of the confidence score; and capturing additional image data as a function of flagging the image data; and a second algorithm comprises an algorithm configured to output annotation data associated with the image data; and generating a display data structure, wherein the display data structure comprises at least: a primary window; and an activity window configured to visualize the image data with an overlay of metadata, wherein the metadata comprises annotation data.

52. The method of claim 51, wherein determining a confidence score comprises generating the confidence score using a confidence machine-learning model, wherein the confidence machine-learning model is trained using a confidence training data set.

53. The method of claim 51, wherein capturing additional image data as a function of flagging the image data further comprises: capturing, at the imaging device, in rapid succession a series of images of a subject matter at varying camera lens focal lengths and exposure times; and identifying, by the at least a processor, an image of the series of images of a subject122 Atorney Docket No. 1519-174PCT1matter with a highest focus quality for a selected region of interest.

54. The method of claim 51, wherein the second algorithm comprises one or more of: an algorithm configured to determine a quality metric for the image data; an algorithm configured to identify different cell groups in one or more image data; and an algorithm configured to generate a color gamut correction for one or more image data.

55. The method of claim 51, wherein the activity window and the primary window present adjacent image data, wherein: the activity window displays altered image data; and the primary window displays the image data.

56. The method of claim 51, further comprising: segmenting the image data using a segmentation tool; displaying, at a display device, the segmented image data at the activity window; receiving, by the at least a processor, a user input, wherein the user input comprises a selection of one or more segments of the segmented image data; and displaying, at the display device, the selection of one or more segments of the segmented image data at the primary window.

57. The method of claim 56, wherein: the activity window comprises: a first activity window, wherein the first activity window comprises a first segmented image datum; and a second activity window, wherein the second activity window comprises a second segmented image datum; and the method further comprises: receiving, by the at least a processor, a user input, wherein the user input comprises a selection of one or more segments of the first segmented image datum and a selection of one or more of the segments of the second segmented image datum; and displaying, at the primary window, the selection of one or more segments of the first segmented image datum and the selection of the one or more segments of the second segmented image datum.

58. The method of claim 51, further comprising:123 Atorney Docket No. 1519-174PCT1receiving, by the at least a processor, a user input, wherein the user input comprises a selected region of interest, wherein the selected region of interest is highlighted as a function of a drag-and-drop operation using a mouse, wherein: the user initiates the selection by clicking on a starting point within the activity window; and drags a cursor to an endpoint, thereby defining the region of interest; and displaying, at a display device, the region of interest at the primary window.

59. The method of claim 58, further comprising: receiving, by at the at least a processor, a user input, wherein the user input comprises a selection of a portion of the image data within the activity window to perform an image processing operation as a function of the second algorithm; applying the second algorithm to the image data; and displaying, at the display device an enhanced image at the primary window.

60. The method of claim 51, further comprising enabling a user, using an interactive display device, to zoom and pan over the display data structure.124 Atorney Docket No. 1519-174PCT1

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