Systems and methods for image analysis - Patents.com

Abstract

The present disclosure provides a method and system for image analysis. The method may be implemented by a gateway device between an image management platform and an image analysis service platform. The method may include obtaining a request to analyze target image data stored in the image management platform. In response to the request, the method may include generating an image analysis task for the target image data. The image analysis task may include a storage address of the target image data in the image management platform. The method may further include obtaining the target image data from the storage address and sending the image analysis task to the image analysis service platform to instruct the image analysis service platform to perform image analysis on the target image data.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to Chinese Patent Application No. 202210443625.X, filed on April 26, 2022, the contents of which are incorporated herein by reference.

[0002] The present disclosure relates generally to image processing, and more particularly to systems and methods for image analysis. [Background technology]

[0003] Through data processing technologies (e.g., cloud computing technology, big data technology, mobile Internet technology, etc.), the image management platform can realize centralized storage, filing, sharing, etc. of local image data and diagnostic reports. Users can use mobile devices to interact with the image management platform to search, read, and share image data and diagnostic reports. Summary of the Invention [Problem to be solved by the invention]

[0004] In some cases, the image management platform may send image data to a third-party image analysis service platform to perform image analysis on the image data. In recent years, with the development of AI technology, several AI image analysis service platforms have been developed to provide image management platforms with services such as image classification, detection, identification, and segmentation. Currently, the image management platform needs to send the received image data to the AI ​​image analysis service platform and instruct the AI ​​image analysis service platform to perform image analysis. However, information leakage may occur during the data transmission process, and the transmission of image data requires a lot of computing resources and network bandwidth. [Means for solving the problem]

[0005] In one aspect of the disclosure, a method for image analysis is provided. The method may be implemented by a gateway device between an image management platform and an image analysis service platform. The method may include obtaining a request to analyze target image data stored in the image management platform. The method may include generating an image analysis task for the target image data in response to the request. The image analysis task may include a storage address of the target image data in the image management platform. The method may further include obtaining the target image data from the storage address and sending the image analysis task to the image analysis service platform to instruct the image analysis service platform to perform image analysis on the target image data.

[0006] In some embodiments, generating an image analysis task for the target image data may include determining whether one or more features of the target image data satisfy a first condition, and generating an image analysis task for the target image data in response to determining that one or more features of the target image data satisfy the first condition.

[0007] In some embodiments, generating an image analysis task for the target image data may include determining whether user permissions corresponding to the request satisfy a second condition, and generating an image analysis task for the target image data in response to determining that user permissions corresponding to the request satisfy the second condition.

[0008] In some embodiments, generating an image analysis task for the target image data may include obtaining a plurality of candidate algorithms, determining a target algorithm from the plurality of candidate algorithms based on the target image data, and generating an image analysis task for the target image data based on the target algorithm.

[0009] In some embodiments, determining a target algorithm from a plurality of candidate algorithms based on the target image data may include: determining, for each of the plurality of candidate algorithms, at least one required requirement and at least one optional requirement for input of the candidate algorithm; selecting at least one candidate algorithm from the plurality of candidate algorithms, where the target image data satisfies the at least one required requirement of each selected candidate algorithm; and determining a target algorithm from the at least one selected candidate algorithm based on the target image data and the at least one optional requirement of each selected candidate algorithm.

[0010] In some embodiments, determining a target algorithm from a plurality of candidate algorithms based on the target image data may include obtaining correspondences between the plurality of candidate algorithms and a plurality of reference feature vectors representing a plurality of sets of reference image data, determining a target feature vector representing the target image data, and determining a target algorithm from the plurality of candidate algorithms based on the target feature vector and the correspondences.

[0011] In some embodiments, determining a target algorithm from a plurality of candidate algorithms based on the target image data may include obtaining an algorithm determination model, where the algorithm determination model is a trained machine learning model, and determining the target algorithm based on the target image data using the algorithm determination model.

[0012] In some embodiments, the method may further include obtaining an analysis summary of the image analysis results of the target image data from the image analysis service platform, and storing the analysis summary in a storage component of the gateway device.

[0013] In some embodiments, the method may further include obtaining, from the user terminal, a second request for obtaining image analysis results of the target image data, and sending the second request to the image analysis service platform to instruct the image analysis service platform to send the image analysis results to the user terminal.

[0014] In some embodiments, the gateway device may be integrated into an image management platform.

[0015] In another aspect of the present disclosure, a system for image analysis is provided. The system may include a gateway device between an image management platform and an image analysis service platform. The system may include at least one storage device including a set of instructions and at least one processor configured to communicate with the at least one storage device. When executing the set of instructions, the at least one processor may be configured to direct the system to perform an operation. The operation may include obtaining a request to analyze target image data stored in the image management platform. The operation may include generating an image analysis task for the target image data in response to the request. The image analysis task may include a storage address of the target image data in the image management platform. The operation may include obtaining the target image data from the storage address and sending the image analysis task to the image analysis service platform to instruct the image analysis service platform to perform image analysis on the target image data.

[0016] In yet another aspect of the present disclosure, a gateway device between an image management platform and an image analysis service platform is provided. The gateway device may include an acquisition module, a generation module, and a transmission module. The acquisition module may be configured to acquire a request to analyze target image data stored in the image management platform. The generation module may be configured to generate an image analysis task for the target image data in response to the request. The image analysis task may include a storage address of the target image data in the image management platform. The transmission module may be configured to acquire the target image data from the storage address and transmit the image analysis task to the image analysis service platform to instruct the image analysis service platform to perform image analysis on the target image data.

[0017] In yet another aspect of the present disclosure, a non-transitory computer readable medium is provided. The non-transitory computer readable medium may be included in a gateway device between an image management platform and an image analysis service platform. The non-transitory computer readable medium may include executable instructions that, when executed by at least one processor, instruct the at least one processor to perform a method. The method may include obtaining a request to analyze target image data stored in the image management platform. The method may include generating an image analysis task for the target image data in response to the request. The image analysis task may include a storage address of the target image data in the image management platform. The method may further include obtaining the target image data from the storage address and sending the image analysis task to the image analysis service platform to instruct the image analysis service platform to perform image analysis on the target image data.

[0018] In yet another aspect of the present disclosure, a method for image analysis is provided. The method may be implemented by an image analysis service platform. The method may include obtaining an image analysis task for target image data. The image analysis task may include a storage address of the target image data in an image management platform. The method may include obtaining the target image data from the storage address in response to the image analysis task. The method may further include performing image analysis on the target image data.

[0019] In some embodiments, image analysis tasks relating to target image data may be transmitted from a gateway device between the image management platform and the image analysis service platform.

[0020] In some embodiments, the method may further include generating an analysis summary of the image analysis results of the target image data, and transmitting the analysis summary to the gateway device.

[0021] In some embodiments, the method may further include obtaining, from a user terminal or a gateway device, a request to obtain image analysis results of the target image data, and transmitting the image analysis results to the user terminal.

[0022] In yet another aspect of the present disclosure, a system for image analysis is provided. The system may include an image analysis service platform. The system may include at least one storage device including a set of instructions and at least one processor configured to communicate with the at least one storage device. When executing the set of instructions, the at least one processor may be configured to direct the system to perform an operation. The operation may include obtaining an image analysis task for the target image data. The image analysis task may include a storage address of the target image data in the image management platform. The operation may include obtaining the target image data from the storage address in response to the image analysis task. The operation may further include performing image analysis on the target image data.

[0023] In yet another aspect of the present disclosure, an image analysis service platform is provided. The image analysis service platform may include an acquisition module and an analysis module. The acquisition module may be configured to acquire an image analysis task for target image data. The image analysis task may include a storage address of the target image data in the image management platform. The acquisition module may be further configured to acquire the target image data from the storage address in response to the image analysis task. The analysis module may be configured to perform image analysis on the target image data.

[0024] In yet another aspect of the present disclosure, a non-transitory computer readable medium is provided. The non-transitory computer readable medium may be included in a gateway device between an image management platform and an image analysis service platform. The non-transitory computer readable medium may include executable instructions that, when executed by at least one processor, instruct the at least one processor to perform a method. The method may include obtaining an image analysis task for target image data. The image analysis task may include a storage address of the target image data in the image management platform. The method may include obtaining the target image data from the storage address in response to the image analysis task. The method may further include performing image analysis on the target image data.

[0025] Additional features are set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and the accompanying drawings, or may be learned by the practice or operation of the embodiments. The features of the disclosure may be realized and attained by practice or use of various aspects of the methods, instrumentalities, and combinations that are described in the detailed examples set forth below.

[0026] The present disclosure will now be further described with respect to exemplary embodiments, which will be described in detail with reference to the drawings, which are non-limiting exemplary embodiments, and in which like reference numerals represent like structure throughout the several views of the drawings. [Brief description of the drawings]

[0027] [Figure 1] FIG. 1 is a schematic diagram illustrating an exemplary image analysis system according to some embodiments of the present disclosure. [Diagram 2] FIG. 2 is a block diagram illustrating an example gateway device according to some embodiments of the present disclosure. [Diagram 3] 1 is a flowchart illustrating an exemplary process for image analysis according to some embodiments of the present disclosure. [Figure 4] 1 is a flowchart illustrating an example process for generating an image analysis task according to some embodiments of the present disclosure. [Diagram 5] 1 is a flowchart illustrating an example process for generating an image analysis task according to some embodiments of the present disclosure. [Figure 6] FIG. 6 is a flowchart illustrating an example process 600 for generating an image analysis task according to some embodiments of the present disclosure. [Figure 7] FIG. 2 is a schematic diagram illustrating a structure of an exemplary gateway device according to some embodiments of the present disclosure. [Figure 8] FIG. 1 is a block diagram illustrating an example image analysis service platform according to some embodiments of the present disclosure. [Figure 9] 1 is a flowchart illustrating an exemplary process for image analysis according to some embodiments of the present disclosure. [Figure 10] 1 is a flowchart illustrating an exemplary process for image analysis according to some embodiments of the present disclosure; [Figure 11] FIG. 1 is a schematic diagram illustrating an exemplary computing device according to some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0028] In the following detailed description, numerous specific details are set forth as examples to provide a thorough understanding of the relevant disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be practiced without such details. In other instances, well-known methods, procedures, systems, components, and / or circuits have been described at a relatively high level without detail to avoid unnecessarily obscuring aspects of the present disclosure. Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the present disclosure. Thus, the present disclosure is not limited to the embodiments shown, but is to be accorded the widest scope consistent with the claims.

[0029] The terms used herein are intended to describe specific exemplary embodiments only and are not intended to be limiting. As used herein, the singular forms "a", "an" and "the" may be intended to include the plural unless the context clearly indicates otherwise. It will be further understood that the terms "comprise", "comprises", and / or "comprising", "include", "includes", and / or "including", as used herein, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0030] When a unit, engine, module, or block is referred to as being "on," "connected," or "coupled" to another unit, engine, module, or block, it will be understood that it may be directly connected or coupled to, or in communication with, the other unit, engine, module, or block, or there may be intervening units, engines, modules, or blocks, unless the context clearly dictates otherwise. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0031] These and other features and characteristics of the present disclosure, as well as the method of operation and function of the associated elements of construction, and the combination of parts and economies of manufacture, will become more apparent from a consideration of the following description taken in conjunction with the accompanying drawings, all of which form a part of this disclosure. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended to limit the scope of the present disclosure. It is understood that the drawings are not to scale.

[0032] In some cases, the image management platform may send image data to a third-party image analysis service platform (e.g., an AI analysis service platform) to perform image analysis on the image data. Currently, the image management platform needs to send the received image data to the AI ​​image analysis service platform and instruct the AI ​​analysis service platform to perform image analysis. However, there are problems with existing technologies, such as how to ensure that the image data sent to the AI ​​analysis service platform is authenticated, how to ensure that the image data sent can be processed by an AI algorithm, how to ensure that the image data sent is not redundant, how to ensure that the AI ​​analysis results are fully stored, accurately interpreted, and presented, how to quickly send the AI ​​analysis results to users without redundant data, and how to reduce changes to the image management platform when adding or updating an AI analysis service platform.

[0033] To solve the above problems, the present disclosure provides a system and method for image analysis. The method may be implemented by a gateway device between an image management platform and an image analysis service platform. The method may include obtaining a request to analyze target image data stored in the image management platform. The method may include generating an image analysis task for the target image data in response to the request. The image analysis task may include a storage address of the target image data in the image management platform. Furthermore, the method may include sending the image analysis task to the image analysis service platform to instruct the image analysis service platform to obtain the target image data from the storage address and perform image analysis on the target image data.

[0034] According to some embodiments of the present disclosure, by introducing a gateway device, only the image analysis task needs to be sent to the image analysis service platform, which can avoid a strong coupling between the image management platform and the image analysis service platform. By sending the image analysis task to the image analysis service platform, the image analysis service platform can obtain only the target image data, which can prevent the image analysis service platform from obtaining other image data in the image management platform, thus improving the privacy and security of the image data stored in the image management platform. Furthermore, only the image analysis task and the target image data need to be sent to the image analysis service platform, which can avoid sending redundant data, thus improving the efficiency of image analysis and reducing the load on the image analysis platform. Furthermore, the method can include sending the image analysis task to the image analysis service platform to obtain the target image data from the storage address and instruct the image analysis service platform to perform image analysis on the target image data.

[0035] FIG. 1 is a schematic diagram illustrating an exemplary image analysis system 100 according to some embodiments of the present disclosure.

[0036] In some embodiments, image analysis system 100 may be configured to manage and / or analyze image data stored in and / or retrieved by image analysis system 100. The image data may include medical image data (e.g., scan data, medical images, etc.) of a subject, surveillance images, or the like, or any combination thereof. It should be noted that the description of the image analysis system is merely exemplary and is not intended to limit the scope of the present disclosure. For example, image analysis system 100 may be configured to manage and / or analyze text data, audio data, video data, etc.

[0037] In some embodiments, the image data may be acquired by an imaging device. For example, the imaging device may include a medical imaging device that generates or provides image data by scanning a subject or at least a portion of a subject. In some embodiments, the imaging device may include a single modality imaging device. For example, the imaging device may include a Positron Emission Tomography (PET) device, a Single Photon Emission Computed Tomography (SPECT) device, a Computed Tomography (CT) device, a Magnetic Resonance Imaging (MRI) device, a Digital Subtraction Angiography (DSA) system, an Intravascular Ultrasound (IVUS) device, or the like. In some embodiments, the imaging device may include a multi-modality imaging device. Exemplary multi-modality imaging devices can include positron emission tomography-computed tomography (PET-CT) devices, positron emission tomography-magnetic resonance imaging (PET-MRI) devices, single photon emission computed tomography-computed tomography (SPECT-CT) devices, digital subtraction angiography-computed tomography (DSA-CT) devices, digital subtraction angiography-positron emission tomography (DSA-PET) devices, digital subtraction angiography-magnetic resonance imaging (DSA-MRI) devices, single photon emission computed tomography-magnetic resonance imaging (SPECT-MRI) devices, etc. Multi-modality imaging devices can perform multi-modality imaging simultaneously or sequentially.

[0038] In some embodiments, the image data may be pre-generated and stored in image analysis system 100 (e.g., a storage device of image analysis system 100). Alternatively, the image data may be pre-generated and stored in an external storage device (e.g., a storage device of the imaging device). Image analysis system 100 can retrieve the image data from the external storage device.

[0039] 1, image analysis system 100 may include a user terminal 110, an image management platform 120, and an image analysis service platform 130. In some embodiments, user terminal 110, image management platform 120, and / or image analysis service platform 130 may be connected to and / or communicate with each other via a wireless connection (e.g., a network), a wired connection, or a combination thereof.

[0040] User terminal 110 may provide a user interface through which a user may view information and / or input data and / or instructions (e.g., requests) to image analysis system 100. For example, a user may send a request to other components (e.g., image management platform 120 and / or image analysis service platform 130) via user terminal 110. For example, a user may send a request to image analysis system 100 (e.g., image management platform 120) via user terminal 110 to analyze target image data stored in image management platform 120. As another example, user terminal 110 may be used to receive image analysis results of target image data from image analysis service platform 130 and / or display image analysis results. User terminal 110 may include a mobile device, a personal computer, a tablet computer, a laptop computer, an Internet of Things (IOT) device, or the like, or any combination thereof.

[0041] The image management platform 120 may be configured to manage image data. For example, the image management platform 120 may be used to store the image data. In some embodiments, the image management platform 120 includes a gateway device 122. The gateway device 122 may be used to realize a communication connection (e.g., data transmission) between the image management platform 120 and the image analysis service platform 130. For example, the gateway device 122 may receive a request to analyze target image data stored in the image management platform 120 from the user terminal 110 and / or the image management platform 120. In response to the request, the gateway device 122 may generate an image analysis task regarding the target image data and send the image analysis task to the image analysis service platform 130. The image analysis task may include a storage address of the target image data in the image management platform 120.

[0042] In some embodiments, gateway device 122 may be integrated into image management platform 120 as shown in FIG. 1. For example, gateway device 122 may be a processor, server, or server cluster (including multiple microservers) within image management platform 120. In some embodiments, gateway device 122 may be a device separate from image management platform 120. Further description of gateway devices may also be found elsewhere in this disclosure (e.g., in FIGS. 2-7 and their description).

[0043] In some embodiments, the image management platform 120 may include at least one first storage device (not shown). In some embodiments, the at least one first storage device may include mass storage, removable storage, volatile read and write memory, read only memory (ROM), or the like, or any combination thereof. In some embodiments, the at least one first storage device may store image data.

[0044] The image analysis service platform 130 can provide an image analysis service. For example, the image analysis service platform 130 may be configured to retrieve target image data from a storage address and perform image analysis on the target image data. In some embodiments, the image analysis service platform 130 can perform image analysis using one or more of a plurality of candidate algorithms (e.g., a plurality of trained image analysis models). In some embodiments, the image analysis service platform 130 can include a plurality of analysis devices (e.g., processors, servers), each of which is used to execute one or more particular candidate algorithms. It should be noted that the process of selecting a target algorithm for processing the target image data can be considered as a process of selecting a target analysis device capable of implementing the target algorithm.

[0045] In some embodiments, the image analysis service platform 130 may include at least one second storage device (not shown). In some embodiments, the at least one second storage device may include mass storage, removable storage, volatile read and write memory, read only memory (ROM), or the like, or any combination thereof. In some embodiments, the at least one second storage device may store a plurality of candidate algorithms and analysis results. Further description of the image analysis service platform may also be found elsewhere in this disclosure (e.g., in FIGS. 8-9 and the description thereof).

[0046] In some embodiments, image management platform 120 and / or image analysis service platform 130 may be a single server or a collection of servers. The collection of servers may be centralized or distributed. In some embodiments, image management platform 120 and / or image analysis service platform 130 may be local or remote. In some embodiments, image management platform 120 and / or image analysis service platform 130 may be implemented on a cloud platform.

[0047] In some embodiments, image management platform 120 and / or image analysis service platform 130 may be implemented by a computing device. For example, the computing device may include a processor, storage, input / output (I / O), and communication ports. The processor may execute computer instructions (e.g., program code) and perform functions of image management platform 120 and / or image analysis service platform 130 in accordance with the techniques described herein. The computer instructions may include, for example, routines, programs, objects, components, data structures, procedures, modules, and functions that perform certain functions described herein. In some embodiments, image management platform 120 and / or image analysis service platform 130, or portions of image management platform 120 and / or image analysis service platform 130, may be implemented by portions of user terminal 110.

[0048] In some embodiments, the image management platform 120 and / or the image analysis service platform 130 may include multiple processing devices. Thus, the operations and / or method steps performed by one processing device as described in this disclosure may also be performed by multiple processing devices, either together or separately. For example, in this disclosure, if the image analysis system 100 performs both operation A and operation B, it should be understood that operation A and operation B may also be performed by two or more different processing devices, either together or separately (e.g., a first processing device performs operation A and a second processing device performs operation B, or a first and a second processing device perform operations A and B together).

[0049] It should be noted that the image analysis system 100 is provided for illustrative purposes and is not intended to limit the scope of the present disclosure. Those skilled in the art may make multiple variations and modifications under the teachings of the present disclosure. However, such variations and modifications do not depart from the scope of the present disclosure. For example, the image analysis system 100 may include one or more additional components of the image analysis system 100 described above and / or may omit one or more components. Additionally or alternatively, two or more components of the image analysis system 100 may be integrated into a single component. A component of the image analysis system 100 may be implemented into two or more sub-components.

[0050] 2 is a block diagram illustrating an example gateway device 122 according to some embodiments of the present disclosure. In some embodiments, the gateway device 122 can communicate with a computer-readable storage medium, and the modules of the gateway device 122 can execute instructions stored on the computer-readable storage medium.

[0051] As shown in FIG. 2, the gateway device 122 may include an acquiring module 210, a generating module 220, and a transmitting module 230.

[0052] The acquisition module 210 may be configured to acquire, from the image management platform, a request to analyze target image data stored in the image management platform. The target image data may refer to image data that needs to be analyzed. The request to analyze the target image data may include information about the target subject, information about the target image data, analysis information, or the like, or any combination thereof. Further description regarding acquiring the request to analyze the target image data may be found elsewhere in this disclosure. See, for example, operation 302 and related description thereof.

[0053] The generation module 220 may be configured to generate an image analysis task for the target image data. The image analysis task may include a storage address of the target image data in the image management platform. Further description regarding the generation of the image analysis task may be found elsewhere in this disclosure. See, for example, operation 304 and its associated description.

[0054] The sending module 230 may be configured to send an image analysis task to the image analysis service platform to instruct the image analysis service platform to retrieve the target image data from the storage address and perform image analysis on the target image data. Further description regarding sending the image analysis task may be found elsewhere in this disclosure. See, for example, operation 306 and its associated description.

[0055] The acquisition module 210 may be further configured to acquire an image analysis report of the target image data. The image analysis report may include an analysis summary, an analysis status, an image analysis result, or the like, or any combination thereof. Further description regarding acquiring the image analysis report may be found elsewhere in this disclosure. See, for example, operation 308 and its associated description.

[0056] It should be noted that the above description of the gateway device 122 is provided for illustrative purposes and is not intended to limit the scope of the present disclosure. Those skilled in the art may make various variations and modifications under the guidance of the present disclosure. However, such variations and modifications do not depart from the scope of the present disclosure. In some embodiments, the gateway device 122 may include one or more other modules. For example, the gateway device 122 may include a storage module for storing data generated by the modules in the gateway device 122. In some embodiments, any two of the modules may be combined as a single module, and any one of the modules may be divided into two or more units.

[0057] 3 is a flow chart illustrating an example process 300 for image analysis according to some embodiments of the present disclosure. In some embodiments, the process 300 may be implemented in the gateway device 122 shown in FIG. 2. For example, the process 300 may be stored in a storage device (e.g., a storage device of the gateway device 122, an external storage device) in the form of instructions (e.g., an application) and invoked and / or executed by the gateway device 122 (e.g., a processor of the gateway device 122).

[0058] At 302, the gateway device 122 (eg, the acquisition module 210) may obtain a request to analyze target image data stored in an image management platform.

[0059] Target image data may refer to image data that needs to be analyzed. For example, target image data may include scan data, medical images, etc. of a target subject that needs to be diagnosed and / or treated.

[0060] The target image data analysis request may include information about the target subject, information about the target image data, analysis information, or the like, or any combination thereof. Information about the target subject may include the target subject's identity, gender, name, age, region of interest (ROI), etc. Information about the target image data may include the target image data's identity, type, etc. Analysis information may include analysis method, analysis requirements (e.g., time required), etc.

[0061] In some embodiments, the gateway device 122 can obtain a request from the image management platform. For example, a user (e.g., a requester, a doctor, a technician, etc.) can send a request to the image management platform 120 via the user terminal 110, and the gateway device 122 can obtain the request from the image management platform 120. By way of example only, the requester can select a request initiation option via an image analysis interface of the user terminal 110, and then the image analysis interface may be switched to a selection interface of subject and / or image data. The requester can determine a target subject and / or target image data from the subject and / or image data via the selection interface. That is, the requester can authenticate the image analysis against the target subject and / or target image data. Thus, a request to analyze the target image data may be sent to the image management platform 120, and then the gateway device 122 can obtain the request from the image management platform 120.

[0062] In some embodiments, the gateway device 122 can obtain the request directly from the user terminal 110. For example, a requester can send a request to the gateway device 122 of the image management platform 120 via the user terminal 110.

[0063] At 304, in response to the request, the gateway device 122 (e.g., the generation module 220) can generate an image analysis task for the target image data. The image analysis task can include a storage address of the target image data in the image management platform.

[0064] An image analysis task may be used to instruct the image analysis service platform to analyze the target image data and include rules or requirements that specify how to analyze the target image data. An image analysis task may include a task type, an analysis algorithm, an identification of a target analysis device, a storage address, a task transmission mode, or the like, or any combination thereof. As used herein, a task type may include processing the target image data and / or analyzing the target image data. An analysis algorithm (also referred to as a target algorithm) may be used to process the target image data. A target analysis device may be used to perform image analysis. For example, a target analysis device may correspond to an analysis algorithm and execute the analysis algorithm to process the target image data.

[0065] In some embodiments, the gateway device 122 may determine whether an image analysis task needs to be generated. For example, the gateway device 122 may obtain one or more features of the target image data based on a request to analyze the target image data, and determine whether the one or more features of the target image data satisfy a first condition. The first condition may be used to determine the availability of the target image data, such as whether the target image data can be processed by an image analysis service platform. If the one or more features of the target image data satisfy the first condition, the gateway device 122 may determine that an image analysis task needs to be generated and generate an image analysis task for the target image data.

[0066] As another example, the gateway device 122 may obtain user permissions corresponding to the request to analyze the target image data and determine whether the user permissions corresponding to the request satisfy a second condition. If the user permissions corresponding to the request satisfy the second condition, the gateway device 122 may determine that an image analysis task needs to be generated and may generate the image analysis task regarding the target image data.

[0067] As yet another example, the gateway device 122 may determine whether image analysis has been performed on the target image data. If image analysis has not been performed on the target image data (i.e., the target image data has been analyzed), the gateway device 122 may determine that an image analysis task needs to be generated and may generate an image analysis task on the target image data.

[0068] In some embodiments, the gateway device 122 may generate an image analysis task for the target image data based on the analysis algorithm. For example, the gateway device 122 may obtain a plurality of candidate algorithms and determine a target algorithm (i.e., an analysis algorithm) from the plurality of candidate algorithms based on the target image data. Further, the gateway device 122 may generate an image analysis task for the target image data based on the target algorithm. Further description regarding the generation of an image analysis task may also be provided elsewhere in this disclosure (e.g., FIGS. 4-7 and the description thereof).

[0069] In some embodiments, the gateway device 122 can generate image analysis tasks for the target image data based on custom rules. The custom rules may relate to the preferences, requirements, and / or habits of a user (e.g., a requester). In some embodiments, the custom rules may be set by a user or determined by a common rules engine (e.g., a subordinate processing device, part of the gateway device 122, or an image management platform). For example, when a custom rule is added or modified, the gateway device 122 can use the common rules engine to modify a configuration file corresponding to the added or modified custom rule without modifying the data processing process performed by the gateway device 122.

[0070] In some embodiments, the gateway device 122 may include a task constructor. When an image analysis task needs to be generated, the gateway device 122 may generate the image analysis task using the task constructor. For example, the gateway device 122 may generate the image analysis task using the task constructor based on an analysis algorithm, a storage address of the target image data in the image management platform, and the like.

[0071] At 306, the gateway device 122 (e.g., the transmitting module 230) can send an image analysis task to the image analysis service platform to instruct the image analysis service platform to retrieve the target image data from the storage address and perform image analysis on the target image data.

[0072] In some embodiments, when an image analysis task is generated, the gateway device 122 can transmit the image analysis task to the image analysis service platform. For example, when an image analysis task is generated, the gateway device 122 can immediately transmit the image analysis task to the image analysis service platform. As another example, the gateway device 122 can transmit the image analysis task to the image analysis service platform at a specific time. The specific time may be determined based on actual conditions (e.g., the idle time of the image analysis service platform, the time when the image analysis result is required, etc.).

[0073] In some embodiments, the gateway device 122 may include a task database, and the gateway device 122 may store the image analysis tasks in the task database. For example, the task database may be a general relational database such as a Hadoop database (HBase), a Cassandra database, etc. In some embodiments, a plurality of image analysis tasks may be stored in the task database. The gateway device 122 may send the image analysis tasks to the image analysis service platform based on a task queue of the plurality of image analysis tasks in the task database. The task queue may be a general message queue or may be a database table. In some embodiments, the task queue may be established based on a comprehensive evaluation of the plurality of image analysis tasks in the task database. For example, the gateway device 122 may generate a task queue by comprehensively evaluating the timeliness requirements, importance, priority, task start time, etc. of each of the plurality of image analysis tasks in the task database, and send the image analysis tasks to the image analysis service platform based on the task queue. For example, the image analysis tasks in the task queue may be sequentially sent to the image analysis service platform.

[0074] In some embodiments, the gateway device 122 may determine whether an image analysis service platform is available. If the image analysis service platform is available, the gateway device 122 may send an image analysis task to the image analysis service platform.

[0075] As a mere example, the gateway device 122 can determine whether the target analysis device corresponding to the target algorithm has sufficient resources to execute the image analysis task. Note that multiple image analysis tasks may be executed simultaneously or sequentially in the target analysis device. When the target analysis device executes the image analysis task simultaneously with other image analysis tasks, the gateway device 122 needs to determine whether the remaining resources of the target analysis device are sufficient to execute the image analysis task. For example, the gateway device 122 can determine the working status and remaining resources of the target analysis device corresponding to the target algorithm by processing the information of the target analysis device using a processing status reader and a status data parser. As another example, the gateway device 122 can determine the working status and remaining resources of the target analysis device corresponding to the target algorithm by querying the target analysis device through a pre-configured query interface. Furthermore, the gateway device 122 can store the working status and remaining resources of the target analysis device in a storage device (e.g., a task database) of the gateway device 122 to determine whether the image analysis service platform is available. If the remaining resources of the target analysis device are sufficient to perform the image analysis task, the gateway device 122 can send the image analysis task to the image analysis service platform 130 (eg, the target analysis device).

[0076] By assessing the remaining resources of the target analysis device corresponding to the target algorithm, the image analysis task may be processed when the target analysis device is available, thereby improving the efficiency of image analysis.

[0077] In some embodiments, after the image analysis task is sent to the image analysis service platform, the gateway device 122 can instruct the image analysis service platform to retrieve the target image data from the storage address. Thus, only the target image data can be retrieved by the image analysis service platform, which can prevent other image data from being sent to the image analysis service platform, thus improving the privacy and security of other image data.

[0078] In some embodiments, after the target image data is acquired by the image analysis service platform, the image analysis service platform can perform image analysis on the target image data. For example, the image analysis service platform can process the target image data using a target algorithm. If the image analysis service platform includes multiple analysis devices, the image analysis service platform can assign an image analysis task to a target analysis device corresponding to the target algorithm.

[0079] At 308, the gateway device 122 (eg, the acquisition module 210) may acquire an image analysis report of the target image data.

[0080] In some embodiments, after the image analysis is performed on the target image data, the gateway device 122 can obtain an image analysis report of the target image data from the image analysis service platform. The image analysis report can include an analysis summary, an analysis status, an image analysis result, or the like, or any combination thereof.

[0081] The analysis summary may include a summary of the image analysis result and / or important information in the image analysis result. For example, the analysis summary may include diagnosis information (e.g., negative result or positive result) of the ROI (e.g., lesion), prompt information of the ROI, an acquisition method of the image analysis result, etc. In some embodiments, the gateway device 122 may obtain the analysis summary of the target image data from the image analysis service platform and manage the analysis summary. For example, the gateway device 122 may store the analysis summary in a storage component of the gateway device 122. For example, the gateway device 122 may persistently store the analysis summary in a task database of the gateway device 122.

[0082] In some embodiments, the gateway device 122 can transmit the analysis summary to a user terminal (e.g., the user terminal 110). For example, the gateway device 122 can determine whether an image analysis task corresponding to the target subject is stored in the task database based on the information of the target subject. If an image analysis task corresponding to the target subject is stored in the task database, the gateway device 122 can transmit the analysis summary of the target subject to the user terminal 110. As another example, the gateway device 122 can instruct the user terminal 110 to display the analysis summary of the target subject. As yet another example, the gateway device 122 can transmit the analysis summary to the user terminal if the analysis summary satisfies a condition (e.g., includes a positive result of a lesion).

[0083] Obtaining an analysis summary can provide important information (e.g., diagnostic information, prompt information, etc.) about the target image data to the user (e.g., the requester) and can reduce time spent on irrelevant details, thereby improving the user experience and the efficiency of image analysis.

[0084] The analysis status can indicate the analysis progress of the image analysis. For example, when an image analysis task is sent to an image analysis service platform, the gateway device 122 can send a status query request to the image analysis service platform at a preset time and receive the analysis status from the image analysis service platform. As another example, when an image analysis task is executed, the gateway device 122 can receive an analysis status indicating that the image analysis task has been executed. By obtaining the analysis status, the availability of the image analysis service platform and the analysis progress of the image analysis can be determined. Thus, the resources of the image analysis service platform (e.g., the target analysis device) can be reasonably allocated after the image analysis task is completed.

[0085] The image analysis result may refer to a detailed analysis report of the target image data. For example, the image analysis result may include diagnostic information and conclusions of image analysis in the form of a list, an image, a symbol mark, etc. In some embodiments, since the image analysis result includes more information and requires more storage space than the analysis summary, the image analysis result may be stored in the image analysis service platform instead of the gateway device 122. The gateway device 122 may obtain a second request from the user terminal to obtain the image analysis result of the target image data, and may send the second request to the image analysis service platform to instruct the image analysis service platform to send the image analysis result to the user terminal.

[0086] In some embodiments, the image analysis task may include a storage period. After the image analysis task is executed and the storage period is reached, the gateway device 122 may instruct the image analysis service platform to delete the target image data. By deleting the target image data, information leakage caused by persistent storage of the target image data on a third-party platform (e.g., the image analysis service platform 130) may be avoided, thereby improving the privacy and security of the target image data.

[0087] According to some embodiments of the present disclosure, by introducing the gateway device 122, only the image analysis task needs to be sent to the image analysis service platform, and a strong coupling between the image management platform and the image analysis service platform can be avoided. By sending the image analysis task to the image analysis service platform, the image analysis service platform can obtain only the target image data, which can prevent the image analysis service platform from obtaining other image data in the image management platform, thereby improving the privacy and security of the image data. Furthermore, only the image analysis task and the target image data need to be sent to the image analysis service platform, which can avoid the transmission of redundant data, thus improving the efficiency of image analysis and reducing the load on the image analysis platform. Furthermore, by introducing the gateway device, multiple analysis devices and algorithms can be added to the image analysis platform, and the functionality of the image analysis platform can be expanded without changing the existing data storage of the image analysis platform.

[0088] 4 is a flow chart illustrating an example process 400 for generating an image analysis task according to some embodiments of the present disclosure. In some embodiments, the process 400 may be performed to accomplish at least a portion of operation 304 as described in connection with FIG.

[0089] At 402, the gateway device 122 (eg, the generation module 220) may obtain one or more characteristics of the target image data based on a request to analyze the target image data.

[0090] The one or more features of the target image data may include metadata information of the target image data. As used herein, metadata information may include information regarding exam type, exam portion, data owner, privacy level, target subject information, institution information, image storage address, image volume, image quality, image format, image acquisition modality, image acquisition time, or the like, or any combination thereof. Image quality may include image resolution, etc. Image format may include DICOM, nii, etc. Image acquisition modality may include a type of device for acquiring the target image data, such as a CT device, an MR device, a PET device, etc.

[0091] In some embodiments, the request to analyze the target image data may include information about the target subject, such as the identification information of the target subject. The gateway device 122 may obtain one or more features of the target image data based on the information about the target subject (e.g., the identification information of the target subject). For example, after the identification information of the target subject is obtained, the gateway device 122 may obtain the target image data in the image management platform 120 and determine one or more features of the target image data based on the target image data. As another example, if the target image data is stored in the image management platform 120, the one or more features of the target image data may be input and / or stored in the image management platform 120. Thus, the gateway device 122 may retrieve one or more features of the target image data from the image management platform 120 based on the identification information of the target subject.

[0092] At 404, the gateway device 122 (eg, the generation module 220) may determine whether one or more characteristics of the target image data satisfy a first condition.

[0093] The first condition may be used to determine the availability of the target image data, such as, for example, whether the target image data can be processed by the image analysis service platform (e.g., using an AI algorithm). In some embodiments, the first condition may be related to one or more features. For example, the first condition may be determined by manually or automatically setting one or more thresholds (or reference values) of one or more features. As another example, the first condition may be determined based on an analysis algorithm or an analysis device within the image analysis service platform 130.

[0094] In some embodiments, the gateway device 122 may compare one or more features of the target image data with their corresponding thresholds (or reference values) in the first condition to determine whether the one or more features of the target image data satisfy the first condition. For example, the gateway device 122 may compare the image quantity, image quality, image storage address, image acquisition manner, etc. of the target image data with their corresponding thresholds (or reference values) in the first condition to determine whether the one or more features of the target image data satisfy the first condition.

[0095] By way of example only, the first condition may include that the preset examination portion is the heart, the preset image format is the DICOM format, and the preset image acquisition time is a particular period (e.g., Monday). The gateway device 122 may compare the examination portion, image format, and image acquisition time of the target image data with the preset examination portion, the preset image format, and the preset image acquisition time. That is, the gateway device 122 may determine whether the examination portion of the target image data matches the preset examination portion (e.g., heart), whether the image format of the target image data matches the preset image format (e.g., DICOM format), and whether the image acquisition time of the target image data matches the preset image acquisition time (e.g., Monday). If the examination portion of the target image data is the heart, the image format of the target image data is the DICOM format, and the image acquisition time of the target image data is Monday, the gateway device 122 may determine that one or more features of the target image data meet the first condition, and the process 400 may proceed to operation 406. If the examination portion of the target image data is not the heart, or if the image format of the target image data is not the DICOM format, or if the image acquisition time of the target image data is not on a Monday, the gateway device 122 may determine that one or more features of the target image data do not satisfy the first condition. The gateway device 122 may transmit the determination result that one or more features of the target image data do not satisfy the first condition to the user terminal 110.

[0096] At 406, in response to determining that one or more features of the target image data satisfy the first condition, the gateway device 122 (e.g., the generation module 220) may generate an image analysis task for the target image data.

[0097] For example, the gateway device 122 can generate an image analysis task for the target image data based on an analysis request for the target image data.

[0098] In some embodiments, the gateway device 122 may further determine whether an image analysis task needs to be generated. For example, the gateway device 122 may determine whether a user permission corresponding to the request satisfies a second condition. As another example, the gateway device 122 may determine whether image analysis has been performed on the target image data.

[0099] In some embodiments, the gateway device 122 may generate an image analysis task for the target image data based on an analysis algorithm. Further description regarding the generation of image analysis tasks may be found elsewhere in this disclosure (e.g., in FIG. 3, FIG. 5, FIG. 6, and the description thereof).

[0100] According to some embodiments of the present disclosure, the availability of the target image data can be determined by determining whether one or more features of the target image data satisfy a first condition, which can improve the accuracy and efficiency of generation of image analysis tasks, thereby improving the accuracy and efficiency of image analysis.

[0101] 5 is a flow chart illustrating an example process 500 for generating an image analysis task according to some embodiments of the present disclosure. In some embodiments, the process 500 may be performed to accomplish at least a portion of operation 304 as described in connection with FIG.

[0102] At 502, the gateway device 122 (eg, the generating module 220) may obtain user authorization corresponding to a request to analyze target image data.

[0103] The user permissions may include a first user permission of the user initiating the request (e.g., the requester) and a second user permission of the target image data and / or the target subject. As used herein, the first user permission may include an authority level of the user, and the second user permission may include a privacy level of the target image data, a privacy level of the target subject, a privacy level of a data owner of the target image data, a privacy level of an institution that owns the target image data, etc.

[0104] In some embodiments, when a user interacts with the gateway device 122 (or the image analysis system 100) via a user terminal, the user may be required to log in, and the gateway device 122 may obtain a first user authorization based on the user's login information.

[0105] In some embodiments, the request to analyze the target image data may include information about the target subject, such as the identity of the target subject. The gateway device 122 may obtain the second user authorization based on the information about the target subject (e.g., the identity of the target subject). For example, if the target image data is stored in the image management platform 120, the second user authorization may be entered and / or stored in the image management platform 120 or a user database connected to the image management platform 120. Thus, the gateway device 122 may retrieve the second user authorization from the image management platform 120 or a user database based on the identity of the target subject.

[0106] At 504, the gateway device 122 (eg, the generating module 220) may determine whether the user permissions corresponding to the request satisfy a second condition.

[0107] The second condition may include a correspondence between the first user permission and the second user permission. For example, the correspondence may indicate that a level of the first user permission must be higher than a level of the second user permission.

[0108] In some embodiments, the gateway device 122 may compare the level of the first user permission to the level of the second user permission to determine whether the user permission corresponding to the request satisfies the second condition. For example, the gateway device 122 may compare the user's authority level to the privacy level of the target subject. If the user's authority level is the same as or higher than the privacy level of the target subject, the gateway device 122 may determine that the user permission corresponding to the request satisfies the second condition, and the process 500 may proceed to operation 506. If the user's authority level is not higher than the privacy level of the target subject, the gateway device 122 may determine that the user permission corresponding to the request does not satisfy the second condition, and the gateway device 122 may transmit a determination result that the user permission corresponding to the request does not satisfy the second condition to the user terminal 110.

[0109] At 506, in response to determining that the user permissions corresponding to the request satisfy the second condition, the gateway device 122 (e.g., the generation module 220) may generate an image analysis task regarding the target image data.

[0110] For example, the gateway device 122 can generate an image analysis task for the target image data based on an analysis request for the target image data.

[0111] In some embodiments, the gateway device 122 may further determine whether an image analysis task needs to be generated. For example, the gateway device 122 may determine whether one or more characteristics of the target image data satisfy a first condition. As another example, the gateway device 122 may determine whether image analysis has been performed on the target image data.

[0112] In some embodiments, the gateway device 122 may generate an image analysis task for the target image data based on an analysis algorithm. Further description regarding the generation of image analysis tasks may be found elsewhere in this disclosure (e.g., in FIG. 3, FIG. 4, FIG. 6, and the description thereof).

[0113] According to some embodiments of the present disclosure, the user permission corresponding to the request can be verified by determining whether the user permission corresponding to the request satisfies a second condition, which can improve the security of the target image data and thus improve the accuracy and efficiency of generation of image analysis tasks.

[0114] 6 is a flow chart illustrating an example process 600 for generating an image analysis task according to some embodiments of the present disclosure. In some embodiments, the process 600 may be performed to accomplish at least a portion of operation 304 as described in connection with FIG.

[0115] At 602, the gateway device 122 (eg, the generation module 220) may obtain a plurality of candidate algorithms.

[0116] A candidate algorithm refers to an image analysis algorithm that the image analysis service platform can implement. Each of the multiple candidate algorithms may be used to implement a specific function and / or process one or more specific types of image data. In some embodiments, each of the multiple candidate algorithms may be executed by one analysis device in the image analysis serving platform (e.g., image analysis serving platform 130). That is, the gateway device 122 may obtain multiple candidate algorithms that may be executed by multiple analysis devices in the image analysis serving platform. For example, the gateway device 122 may obtain a list or table for storing multiple candidate algorithms that may be executed by multiple analysis devices in the image analysis serving platform 130.

[0117] In some embodiments, the multiple candidate algorithms may be stored in an algorithm database (e.g., a table, a list, etc.), and the gateway device 122 may retrieve the multiple candidate algorithms from the algorithm database. The algorithm database may include other information of the multiple candidate algorithms. For example, for each of the multiple candidate algorithms, the other information may include storage address, corresponding analysis device, functions, image processing capabilities, requirements for input of the candidate algorithm, etc.

[0118] At 604, the gateway device 122 (eg, the generation module 220) may determine a target algorithm from a plurality of candidate algorithms based on the target image data.

[0119] A target algorithm may be used to process the target image data.

[0120] In some embodiments, for each of the multiple candidate algorithms, the gateway device 122 may determine at least one mandatory requirement and at least one optional requirement for the input of the candidate algorithm. A mandatory requirement may refer to a requirement that the input of the candidate algorithm must satisfy. For example, for a candidate algorithm that is used only to process CT image data, a mandatory requirement may include a requirement that the input includes CT image data. An optional requirement may refer to a non-mandatory requirement for the input of the candidate algorithm. For example, an optional requirement may include a requirement that the noise of the input does not exceed a noise threshold.

[0121] As merely an example, for each of the multiple candidate algorithms, the gateway device 122 may obtain from the algorithm database at least one required requirement and at least one optional requirement for the input of the candidate algorithm. Then, the gateway device 122 may select at least one candidate algorithm from the multiple candidate algorithms. The target image data may satisfy the at least one required requirement of each selected candidate algorithm. The gateway device 122 may further determine a target algorithm from the at least one selected candidate algorithm based on the target image data and the at least one optional requirement of each selected candidate algorithm. For example, if the target image data satisfies each optional requirement of one or more specific candidate algorithms, the gateway device 122 may randomly select a candidate algorithm from the one or more specific candidate algorithms as a target algorithm. As another example, the gateway device 122 may determine a score of each of the at least one selected candidate algorithm based on the target image data and the at least one optional requirement of the selected candidate algorithm, and designate the candidate algorithm having the highest score among the at least one selected candidate algorithm as the target algorithm. The score of the selected candidate algorithm may indicate the degree to which the target image data satisfies at least one optional refit of the selected candidate algorithm. The score may be determined based on a scoring rule or a scoring model (e.g., a trained machine learning model). Furthermore, when there are multiple highest scoring candidate algorithms, the gateway device 122 may randomly select one candidate algorithm from the multiple highest scoring candidate algorithms and designate it as the target algorithm.

[0122] In some embodiments, the gateway device 122 can obtain a correspondence between a plurality of candidate algorithms and a plurality of reference feature vectors representing a plurality of sets of reference image data. The correspondence may be determined based on a past usage record. For example, if a candidate algorithm is frequently used to process a type of reference feature vector (or a type of set of reference image data), it may be considered that there is a correspondence between the candidate algorithm and the type of reference feature vector (or a type of set of reference image data). The correspondence may be shown in a table, a diagram, a mathematical function, etc. The reference feature vector of the set of reference image data may be determined based on the image type, the inspection portion, the features of the reference subject, etc. corresponding to the set of reference image data. The gateway device 122 can determine a target feature vector representing the target image data. The determination of the target feature vector may be similar to the determination of the reference feature vector.

[0123] Further, the gateway device 122 may determine a target algorithm from a plurality of candidate algorithms based on the target feature vector and the correspondence. For example, the correspondence may be shown in a table including a plurality of rows, and each row may record a reference feature vector and a corresponding candidate algorithm. The gateway device 122 may search for a target algorithm by looking up the table (i.e., the correspondence) based on the target feature vector. For example, the gateway device 122 may determine one or more reference feature vectors from a plurality of reference feature vectors. The similarity between each of the one or more reference feature vectors and the target feature vector may exceed a similarity threshold. The similarity may be determined based on a similarity rule or a similarity model (e.g., a trained machine learning model). The similarity threshold may be determined based on a system default setting (e.g., statistical information) or may be manually set by a user (e.g., a requester, an engineer, a doctor, a physicist, etc.), such as 0.6, 0.7, 0.8, 0.9, etc. The gateway device 122 may determine a target algorithm based on the correspondence and a reference feature vector that has the highest similarity among the one or more reference feature vectors. For example, the candidate algorithm corresponding to the most similar reference feature vector may be designated as the target algorithm.

[0124] In some embodiments, the gateway device 122 can obtain an algorithmic decision model. The gateway device 122 can use the algorithmic decision model to decide a target algorithm based on the target image data. For example, the gateway device 122 can input the target image data of the target subject into the algorithmic decision model, and the algorithmic decision model can output the target algorithm. As another example, the gateway device 122 can determine a target feature vector representing the target image data, and input the target feature vector into the algorithmic decision model. The algorithmic decision model can output the target algorithm.

[0125] In some embodiments, the algorithm decision model may refer to a process or algorithm used to determine a recommended algorithm to be used to process image data. The algorithm decision model may be a trained machine learning model. Exemplary machine learning models may include a convolutional neural network (CNN) model, a recurrent neural network (RNN) model, a long short-term memory (LSTM) network model, a fully convolutional neural network (FCN) model, a generative adversarial network (GAN) model, or the like, or any combination thereof.

[0126] In some embodiments, the gateway device 122 may obtain the algorithmic decision model from a storage device of the gateway device 122 or a third-party database. In some embodiments, the algorithmic decision model may be generated by the gateway device 122 or another computing device according to a machine learning algorithm. In some embodiments, the algorithmic decision model may be generated by a computing device (e.g., the gateway device 122) by training an initial model using a plurality of training samples. Each of the plurality of training samples may include sample image data of a sample subject (or a sample feature vector of the sample image data) and a sample algorithm used to process the sample image data. The sample algorithm may be determined automatically or manually. For example, a user may determine a sample algorithm corresponding to the sample image data (or a sample feature vector) via a user terminal (e.g., the user terminal 110) that displays the sample image data (or the sample feature vector). As another example, the gateway device 122 may determine a sample algorithm corresponding to the sample image data (or the sample feature vector) based on an analysis record of the sample image data.

[0127] At 606, the gateway device 122 (eg, the generation module 220) may generate an image analysis task for the target image data based on the target algorithm.

[0128] For example, the generated image analysis task for the target image data may include information (eg, identification information) regarding the target algorithm.

[0129] In some embodiments, gateway device 122 may further determine whether an image analysis task needs to be generated by performing process 400 and / or process 500. Further description regarding the generation of an image analysis task may also be found elsewhere in this disclosure (e.g., in FIGS. 3-5 and their description).

[0130] According to some embodiments of the present disclosure, a target algorithm may be automatically determined from a plurality of candidate algorithms based on the target image data, and image analysis tasks for the target image data may be generated based on the target algorithm, thereby improving the accuracy and efficiency of image analysis.

[0131] FIG. 7 is a schematic diagram illustrating the structure of an example gateway device 122 according to some embodiments of the present disclosure.

[0132] 7, the gateway device 122 may include a request receiver 1221, a request result retriever 1222, an image data retriever 1223, a data availability checker 1224, a privacy and authentication filter 1225, a custom rule filter 1226, an algorithm filter 1227, a task constructor 1228, a task allocator 1229, a processing status reader 1230, a status data parser 1231, a task database 1232, etc. It should be noted that the gateway device 122 is provided for illustrative purposes and is not intended to limit the scope of the present disclosure.

[0133] The request receiver 1221 can obtain an analysis request for target image data stored in the image management platform 120 (e.g., image database 124) from the image management platform 120 (or user terminal 110). The request result retriever 1222 can determine whether the target image data has been analyzed, for example, by determining whether the task database 1232 (or a storage device of the image management platform 120) includes a past image analysis task corresponding to the target image data. If the task database 1232 includes a past image analysis task corresponding to the target image data, the request result retriever 1222 can obtain an analysis summary and / or an analysis status corresponding to the past image analysis task. If the task database 1232 does not include an image analysis task corresponding to the request, a new image analysis task corresponding to the request can be generated. The image data retriever 1223 can obtain the target image data and information of the target image data (e.g., one or more features, user permission, etc.) from the image database 124. The data availability checker 1224 can determine the availability of the target image data by determining whether one or more features of the target image data satisfy a first condition. If one or more features of the target image data satisfy a first condition, the privacy and authentication filter 1225 can determine whether the user permission corresponding to the request satisfies a second condition. If the user permission corresponding to the request satisfies the second condition, the custom rule filter 1226 can determine whether the request satisfies a custom rule. If the request satisfies the custom rule, the algorithm filter 1227 can determine a target algorithm from a plurality of candidate algorithms based on the target image data. Thus, the task constructor 1228 can generate an image analysis task corresponding to the request. The task allocator 1229 can allocate the image analysis task to a target analysis device corresponding to the target algorithm.That is, the gateway device 122 can send the image analysis task to the image analysis service platform 130.

[0134] In some embodiments, before an image analysis task is sent to the image analysis service platform 130, the gateway device 122 can determine whether the remaining resources of the target analysis device are sufficient to perform the image analysis task by processing information of the target analysis device using the processing status reader 1230 and the status data parser 1231.

[0135] 8 is a block diagram illustrating an example image analysis service platform 130 according to some embodiments of the present disclosure. In some embodiments, the image analysis service platform 130 can be in communication with a computer-readable storage medium, and modules of the image analysis service platform 130 can execute instructions stored on the computer-readable storage medium.

[0136] As shown in FIG. 8, the image analysis service platform 130 can include an acquisition module 810 and an analysis module 820 .

[0137] The acquisition module 810 may be configured to acquire an image analysis task for the target image data. The image analysis task may include a storage address of the target image data in the image management platform. Further description regarding the acquisition of the image analysis task may be found elsewhere in this disclosure. See, for example, operation 902 and its associated description.

[0138] The acquisition module 810 may be configured to acquire the target image data from the storage address. Further description regarding acquiring the target image data may be found elsewhere in this disclosure. See, for example, operation 904 and its associated description.

[0139] The analysis module 820 may be configured to perform image analysis on the target image data. For example, the analysis module 820 may search for a target analysis device corresponding to a target algorithm based on the image analysis task, and instruct the target analysis device to perform image analysis on the target image data using the target algorithm. Further description regarding the analysis of the target image data may be found elsewhere in this disclosure. See, for example, operation 906 and its associated description.

[0140] It should be noted that the above description of the image analysis service platform 130 is provided for illustrative purposes and is not intended to limit the scope of the present disclosure. Those skilled in the art may make various variations and modifications under the guidance of the present disclosure. However, such variations and modifications do not depart from the scope of the present disclosure. In some embodiments, the image analysis service platform 130 may include one or more other modules. For example, the image analysis service platform 130 may include a storage module for storing data generated by the modules in the image analysis service platform 130. In some embodiments, any two of the modules may be combined as a single module, and any one of the modules may be divided into two or more units.

[0141] Figure 9 is a flowchart illustrating an example process 900 for image analysis according to some embodiments of the present disclosure. In some embodiments, the process 900 may be implemented in the image analysis service platform 130 shown in Figure 8. For example, the process 900 may be stored in a storage device (e.g., a storage device of the image analysis service platform 130, an external storage device) in the form of instructions (e.g., an application) and invoked and / or executed by the image analysis service platform 130.

[0142] At 902, the image analysis service platform 130 (e.g., the acquisition module 810) can acquire an image analysis task for the target image data. The image analysis task can include a storage address of the target image data in the image management platform.

[0143] The image analysis task may be used to instruct the image analysis service platform to analyze target image data and include rules or requirements that specify how to analyze the target image data. Further description of the image analysis task may be found elsewhere in this disclosure (e.g., in Figures 3-6 and the description thereof).

[0144] In some embodiments, the gateway device 122 can send the image analysis task to the image analysis service platform 130, and the image analysis service platform 130 can retrieve the image analysis task.

[0145] At 904, in response to the image analysis task, the image analysis service platform 130 (eg, the acquisition module 810) may acquire the target image data from the storage address.

[0146] For example, the image analysis service platform 130 can retrieve the target image data from the image management platform (eg, the image database 124) based on a storage address of the target image data in the image management platform.

[0147] At 906, the image analysis service platform 130 (eg, the analysis module 820) may perform image analysis on the target image data.

[0148] For example, the image analysis service platform 130 can search for a target analysis device that corresponds to a target algorithm based on the image analysis task (e.g., identification information of the target analysis device) and instruct the target analysis device to perform image analysis on the target image data using the target algorithm.

[0149] In some embodiments, after image analysis is performed on the target image data, the image analysis service platform 130 can generate an image analysis report of the target image data. The image analysis report can include an analysis summary, an analysis status, an image analysis result, or the like, or any combination thereof. Further description of the image analysis report may also be found elsewhere in this disclosure (e.g., FIG. 3 and its description).

[0150] In some embodiments, the image analysis service platform 130 may send the image analysis report to the gateway device 122 (or image management platform 120) and / or the user terminal 110. For example, the gateway device 122 and / or the user terminal 110 may send a second request to obtain the image analysis result of the target image data. In response to the second request, the image analysis service platform 130 may send the image analysis report to the gateway device 122 and / or the user terminal 110.

[0151] In some embodiments, the image analysis task may include a retention period. After the image analysis task is performed and the retention period is reached, the image analysis service platform 130 may delete the target image data. By deleting the target image data, information leakage caused by persistent storage of the target image data on a third-party platform (e.g., the image analysis service platform 130) may be avoided, thereby improving the privacy and security of the target image data.

[0152] FIG. 10 is a flow chart illustrating an example process 1000 for image analysis, according to some embodiments of the present disclosure.

[0153] At 1002 , a user terminal 110 may send a request to an image management platform 120 to analyze target image data stored in the image management platform 120 .

[0154] At 1004 , the gateway device 122 may obtain a request from the image management platform 120 to analyze target image data stored in the image management platform 120 .

[0155] At 1006, the gateway device 122 can determine whether the target image data has been analyzed, for example, by determining whether a task database (or a storage device of the image management platform 120) contains a past image analysis task that corresponds to the target image data.

[0156] At 1008, if the task database includes past image analysis tasks corresponding to the target image data, the gateway device 122 may obtain an analysis summary and / or an analysis status corresponding to the past image analysis task.

[0157] At 1010, if the task database does not contain an image analysis task corresponding to the request, the gateway device 122 may obtain the target image data and information about the target image data (e.g., one or more features, user permissions, etc.) from the image management platform 120 (e.g., the image database 124).

[0158] At 1012, the gateway device 122 may determine the availability of the target image data by determining whether one or more characteristics of the target image data satisfy a first condition.

[0159] At 1014, if one or more characteristics of the target image data satisfy a first condition, the gateway device 122 may determine whether the user permissions corresponding to the request satisfy a second condition.

[0160] At 1016, if the user permissions corresponding to the request satisfy a second condition, the gateway device 122 may determine a target algorithm from a plurality of candidate algorithms based on the target image data.

[0161] At 1018, the gateway device 122 may generate an image analysis task corresponding to the request. The image analysis task may include a storage address of the target image data in the image management platform 120.

[0162] At 1020, the gateway device 122 may determine whether an image analysis service platform is available.

[0163] At 1022, if the image analysis service platform is available, the gateway device 122 may send the image analysis task to the image analysis service platform 130.

[0164] At 1024, the image analysis service platform 130 may retrieve the target image data from the storage address.

[0165] At 1026, the image analysis service platform 130 can perform image analysis on the target image data to generate an image analysis report of the target image data. The image analysis report may include an analysis summary, an analysis status, an image analysis result, and the like.

[0166] At 1028, the gateway device 122 may obtain an image analysis report of the target image data from the image analysis service platform 130.

[0167] At 1030 , the gateway device 122 may transmit the analysis summary and / or the analysis status to the user terminal 110 .

[0168] At 1032, the image analysis service platform 130 may delete the target image data.

[0169] At 1034, the user terminal 110 may send a second request to the image analysis service platform 130 to obtain image analysis results of the target image data.

[0170] At 1036, the user terminal 110 may receive the image analysis results from the image analysis service platform 130 and display the image analysis results.

[0171] The processes 300-600, 900, and 1000 may be implemented in the image analysis system 100 shown in FIG. 1. For example, the processes 300-600, 900, and 1000 may be stored in a storage device in the form of instructions (e.g., applications) and called and / or executed by the gateway device 122 and / or the image analysis service platform 130. The operations of the illustrated processes presented below are for illustrative purposes. In some embodiments, the processes 300-600, 900, and 1000 may be accomplished by one or more additional operations not described and / or without one or more operations described. Furthermore, the order of operations of the processes 300-600, 900, and 1000 shown in FIGS. 3-6, 9, and 10 is not intended to be limiting.

[0172] It should be noted that the description of processes 300-600, 900, and 1000 is provided for illustrative purposes and is not intended to limit the scope of the present disclosure. Those skilled in the art can make various variations and modifications under the teachings of the present disclosure. However, these variations and modifications do not depart from the protection of the present disclosure.

[0173] FIG. 11 is a schematic diagram illustrating an example computing device 1100 in accordance with some embodiments of the present disclosure.

[0174] In some embodiments, one or more components of image analysis system 100 may be implemented on computing device 1100. For example, a processing engine may be implemented on computing device 1100 and configured to implement the functions and / or methods disclosed in this disclosure.

[0175] Computing device 1100 may include any components used to implement image analysis system 100 as described in this disclosure. For example, gateway device 122 and / or image analysis service platform 130 may be implemented by hardware, software programs, firmware, or any combination thereof on computing device 1100. For illustrative purposes, only one computer is depicted in FIG. 11, but the computational functionality associated with image analysis system 100 as described in this disclosure may be implemented in a distributed manner by a fleet of similar platforms to distribute the processing load of image analysis system 100.

[0176] The computing device 1100 may include a communication port connected to a network to achieve data communication. The computing device 1100 may include a processor (e.g., a central processing unit (CPU)), a memory, a communication interface, a display unit, and an input device connected by a system bus. The processor of the computing device 1100 may be used to provide computing and control functions. The memory of the computing device 1100 may include a non-volatile storage medium, an internal memory. The non-volatile storage medium may store an operating system and a computer program. The internal memory may provide an environment for the execution of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computing device 1100 may be used for wired or wireless communication with an external terminal. The wireless communication may be realized via Wi-Fi, a mobile cellular network, near field communication (NFC), etc. When the computer program is executed by the processor, a method for determining feature points may be implemented. The display unit of the computing device 1100 may include a liquid crystal display screen or an electronic ink display screen. Input devices of computing device 1100 may include a touch layer placed on the display unit, devices configured on the housing of computing device 1100 (e.g., buttons, trackball, touchpad, etc.), an external keyboard, an external trackpad, an external mouse, etc.

[0177] For illustrative purposes only, only one processor is illustrated in FIG. 11. However, it should be noted that the computing device 1100 in the present disclosure may also include multiple processors. Thus, the operations and / or method steps performed by one processor as described in the present disclosure may also be performed by multiple processors together or separately. For example, it should be understood that if the processor of the computing device 1100 in the present disclosure performs both operation A and operation B, operation A and operation B may also be performed by two or more different processors together or separately (e.g., a first processor performs operation A and a second processor performs operation B, or the first processor and the second processor cooperate to perform operations A and B).

[0178] Having thus described the basic concepts, it will be fairly clear to those skilled in the art after reading this detailed disclosure that the foregoing detailed disclosure is intended to be presented by way of example only and is not limiting. Although not expressly stated herein, various changes, improvements, and modifications may be made and are within the scope of the skilled artisan. These changes, improvements, and modifications are intended to be suggested by this disclosure and are within the spirit and scope of the exemplary embodiments of the disclosure.

[0179] Furthermore, certain terms are used to describe embodiments of the present disclosure. For example, the terms "one embodiment," "one embodiment," and / or "some embodiments" mean that a particular feature, structure, or characteristic described in connection with an embodiment is included in at least one embodiment of the present disclosure. Thus, it is emphasized and should be understood that two or more references to "one embodiment" or "one embodiment" or "alternative embodiments" in various parts of the present disclosure do not necessarily all refer to the same embodiment. Furthermore, certain features, structures, or characteristics may be combined as appropriate in one or more embodiments of the present disclosure.

[0180] Furthermore, the recited order of processing elements or sequences, or the use of numbers, letters, or other designations, are therefore not intended to limit the processes and methods set forth in the claims to any order, unless expressly stated in the claims. While the above disclosure discusses, through various examples, what are presently believed to be various useful embodiments of the present disclosure, it should be understood that such details are for the purpose only, and that the appended claims are not limited to the disclosed embodiments, but on the contrary, are intended to cover modifications and equivalent arrangements that are within the spirit and scope of the disclosed embodiments. For example, implementations of the various components described above may be embodied in hardware devices, but may also be implemented as software-only solutions, such as installation on existing servers or mobile devices.

[0181] Similarly, in the foregoing description of embodiments of the present disclosure, it should be understood that various features may be grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in understanding one or more of the various embodiments of the present invention. However, this method of disclosure should not be interpreted as reflecting an intention that the claimed subject matter requires more features than are expressly recited in each claim. Rather, embodiments of the present invention may reside in less than all features of a single foregoing disclosed embodiment.

[0182] In some embodiments, numbers expressing quantities or properties used to describe and claim certain embodiments of the present application should be understood to be modified in some cases by the terms "about", "approximate" or "substantially". For example, "about", "approximate", or "substantially" may indicate a variation of ±20% of the value it describes, unless otherwise specified. Thus, in some embodiments, the numerical parameters set forth in the specification and appended claims are approximations and may vary depending on the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the present application are approximations, the numerical values ​​set forth in the specific examples are reported as precisely as practicable.

[0183] Each of the patents, patent applications, patent application publications, and other materials, such as articles, books, specifications, publications, documents, things, and / or the like, referenced herein is incorporated herein by reference in its entirety for all purposes, except for any prosecution file history associated therewith, any of which may be inconsistent or conflicting with the present specification, or which may have a limiting effect on the broadest scope of any claims now or in the future associated with the present specification. By way of example, in the event of a conflict or inconsistency between an explanation, definition, and / or use of a term associated with any of the incorporated materials and an explanation, definition, and / or use of a term associated with this document, the explanation, definition, and / or use of the term in this document shall control.

[0184] Finally, it should be understood that the embodiments of the present application disclosed herein are illustrative of the principles of the embodiments of the present application. Other variations that may be used may be within the scope of the present application. Thus, by way of example, and not of limitation, alternative configurations of the embodiments of the present application may be utilized in accordance with the teachings herein. Thus, the embodiments of the present application are not limited to those precisely as shown and described. [Explanation of symbols]

[0185] 100 Image Analysis System 110 User terminal 120 Image Management Platform 122 Gateway Device 124 Image Database 130 Image analysis service platform 210 Acquisition Module 220 Generation Module 230 Transmitting Module 300 processes 400 processes 500 processes 600 processes 810 Acquisition Module 820 Analysis Module 900 Processes 1000 processes 1100 Computing Devices 1221 Request Receiver 1222 Request Result Retriever 1223 Image Data Retriever 1224 Data Availability Checker 1225 Privacy and Authentication Filters 1226 Custom Rule Filter 1227 Algorithm Filter 1228 Task Constructor 1229 Task Allocator 1230 Processing Status Leader 1231 Situation Data Parser 1232 Task Database

Claims

1. 1. A method implemented by a gateway device between an image management platform and an image analysis service platform, comprising: obtaining a request to analyze target image data stored in the image management platform; generating an image analysis task for the target image data in response to the request, the image analysis task including a storage address of the target image data in the image management platform; Retrieving the target image data from the storage address, and sending the image analysis task to the image analysis service platform to instruct the image analysis service platform to perform image analysis on the target image data; A method comprising:

2. The step of generating image analysis tasks for the target image data further comprises: determining whether one or more features of the target image data satisfy a first condition; generating the image analysis task for the target image data in response to determining that the one or more features of the target image data satisfy the first condition; 2. The method of claim 1, comprising:

3. The step of generating image analysis tasks for the target image data further comprises: determining whether a user permission corresponding to the request satisfies a second condition; generating the image analysis task on the target image data in response to determining that the user permissions corresponding to the request satisfy the second condition; 3. The method of claim 1 or 2, comprising:

4. The step of generating image analysis tasks for the target image data further comprises: obtaining a plurality of candidate algorithms; determining a target algorithm from the plurality of candidate algorithms based on the target image data; generating the image analysis task for the target image data based on the target algorithm; The method according to any one of claims 1 to 3, comprising:

5. determining a target algorithm from the plurality of candidate algorithms based on the target image data, determining, for each of the plurality of candidate algorithms, at least one mandatory requirement and at least one optional requirement regarding inputs of the candidate algorithm; selecting at least one candidate algorithm from the plurality of candidate algorithms, wherein the target image data satisfies the at least one required requirement of each selected candidate algorithm; determining the target algorithm from the at least one selected candidate algorithm based on the target image data and the at least one optional requirement of each selected candidate algorithm; 5. The method of claim 4, comprising:

6. determining a target algorithm from the plurality of candidate algorithms based on the target image data, obtaining correspondences between the candidate algorithms and a plurality of reference feature vectors representing a plurality of sets of reference image data; determining a target feature vector representing the target image data; determining the target algorithm from the plurality of candidate algorithms based on the target feature vector and the correspondence relationship; 5. The method of claim 4, comprising:

7. determining a target algorithm from the plurality of candidate algorithms based on the target image data, obtaining an algorithmic decision model, the algorithmic decision model being a trained machine learning model; determining the target algorithm based on the target image data using the algorithm determination model; 5. The method of claim 4, comprising:

8. obtaining an analysis summary of an image analysis result of the target image data from the image analysis service platform; storing the analysis summary in a storage component of the gateway device; 8. The method of claim 1, further comprising:

9. obtaining a second request from a user terminal for obtaining an image analysis result of the target image data; sending the second request to the image analysis service platform to instruct the image analysis service platform to send the image analysis result to the user terminal; 9. The method of claim 1, further comprising:

10. The method according to claim 1 , wherein the gateway device is integrated into the image management platform.

11. A system including a gateway device between an image management platform and an image analysis service platform, at least one storage device containing a set of instructions; and at least one processor configured to communicate with the at least one storage device, wherein when executing the set of instructions, the at least one processor: Obtaining a request to analyze target image data stored in the image management platform; generating, in response to the request, an image analysis task for the target image data, the image analysis task including a storage address of the target image data in the image management platform; Retrieving the target image data from the storage address, and sending the image analysis task to the image analysis service platform to instruct the image analysis service platform to perform image analysis on the target image data; configured to instruct the system to perform an action, system.

12. Generating image analysis tasks for the target image data includes: determining whether one or more features of the target image data satisfy a first condition; generating the image analysis task for the target image data in response to determining that the one or more features of the target image data satisfy the first condition; and The system of claim 11 , comprising:

13. Generating image analysis tasks for the target image data includes: determining whether a user permission corresponding to the request satisfies a second condition; and generating the image analysis task on the target image data in response to determining that the user permissions corresponding to the request satisfy the second condition; and 13. The system of claim 11 or 12, comprising:

14. said generating image analysis tasks for said target image data further comprises: Obtaining a plurality of candidate algorithms; determining a target algorithm from the plurality of candidate algorithms based on the target image data; generating the image analysis task for the target image data based on the target algorithm; 14. The system according to claim 11, comprising:

15. Determining a target algorithm from the plurality of candidate algorithms based on the target image data includes: determining, for each of the plurality of candidate algorithms, at least one mandatory requirement and at least one optional requirement regarding inputs of the candidate algorithm; selecting at least one candidate algorithm from the plurality of candidate algorithms, wherein the target image data satisfies the at least one required requirement of each selected candidate algorithm; determining the target algorithm from the at least one selected candidate algorithm based on the target image data and the at least one optional requirement of each selected candidate algorithm; The system of claim 14, comprising:

16. Determining a target algorithm from the plurality of candidate algorithms based on the target image data includes: obtaining correspondences between the candidate algorithms and a plurality of reference feature vectors representing a plurality of sets of reference image data; determining a target feature vector representing the target image data; determining the target algorithm from the plurality of candidate algorithms based on the target feature vector and the correspondence relationship; The system of claim 14, comprising:

17. Determining a target algorithm from the plurality of candidate algorithms based on the target image data includes: obtaining an algorithmic decision model, the algorithmic decision model being a trained machine learning model; determining the target algorithm based on the target image data using the algorithm determination model; The system of claim 14, comprising:

18. obtaining an analysis summary of an image analysis result of the target image data from the image analysis service platform; storing the analysis summary in a storage component of the gateway device; 18. The system of claim 11, further comprising:

19. obtaining a second request from a user terminal for obtaining an image analysis result of the target image data; sending the second request to the image analysis service platform to instruct the image analysis service platform to send the image analysis result to the user terminal; 19. The system of claim 11, further comprising:

20. 20. The system of claim 11, wherein the gateway device is integrated into the image management platform.

21. A gateway device between an image management platform and an image analysis service platform, an acquisition module configured to acquire a request to analyze target image data stored in the image management platform; a generation module configured to generate an image analysis task for the target image data in response to the request, the image analysis task including a storage address of the target image data in the image management platform; a sending module configured to retrieve the target image data from the storage address and send the image analysis task to the image analysis service platform to instruct the image analysis service platform to perform image analysis on the target image data; A gateway device comprising:

22. 11. A non-transitory computer-readable medium included in a gateway device between an image management platform and an image analysis service platform, the non-transitory computer-readable medium including executable instructions that, when executed by at least one processor, direct the at least one processor to perform a method, the method comprising: obtaining a request to analyze target image data stored in the image management platform; generating an image analysis task for the target image data in response to the request, the image analysis task including a storage address of the target image data in the image management platform; Retrieving the target image data from the storage address, and sending the image analysis task to the image analysis service platform to instruct the image analysis service platform to perform image analysis on the target image data; A non-transitory computer readable medium comprising:

23. 1. A method implemented by an image analysis service platform, comprising: obtaining an image analysis task for a target image data, the image analysis task including a storage address of the target image data in an image management platform; retrieving the target image data from the storage address in response to the image analysis task; performing image analysis on the target image data; A method comprising:

24. The method of claim 23 , wherein the image analysis task for the target image data is transmitted from a gateway device between the image management platform and the image analysis service platform.

25. generating an analysis summary of the image analysis results of the target image data; sending the analysis summary to a gateway device; 25. The method of claim 23 or 24, further comprising:

26. receiving a request from a user terminal or the gateway device to obtain an image analysis result of the target image data; transmitting the image analysis result to the user terminal; 26. The method of any one of claims 23 to 25, further comprising:

27. A system including an image analysis service platform, at least one storage device containing a set of instructions; and at least one processor configured to communicate with the at least one storage device, wherein when executing the set of instructions, the at least one processor: Obtaining an image analysis task for target image data, the image analysis task including a storage address of the target image data in an image management platform; retrieving the target image data from the storage address in response to the image analysis task; performing image analysis on the target image data; configured to instruct the system to perform an action, system.

28. An image analysis service platform, an acquisition module configured to acquire an image analysis task related to target image data, the image analysis task including a storage address of the target image data in an image management platform; an acquisition module, the acquisition module further configured to acquire the target image data from the storage address in response to the image analysis task; an analysis module configured to perform image analysis on the target image data; An image analysis service platform that provides:

29. 1. A non-transitory computer-readable medium included in an image analysis service platform, the non-transitory computer-readable medium including executable instructions that, when executed by at least one processor, direct the at least one processor to perform a method, the method comprising: obtaining an image analysis task for a target image data, the image analysis task including a storage address of the target image data in an image management platform; retrieving the target image data from the storage address in response to the image analysis task; performing image analysis on the target image data; A non-transitory computer readable medium comprising:

Images