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Medical image processing method and device based on artificial intelligence, equipment and medium

A medical imaging and artificial intelligence technology, applied in medical imaging, neural learning methods, healthcare informatics, etc., can solve the problems of machine learning models consuming huge computing resources, medical image processing efficiency is low, and large bandwidth resources are occupied. Achieve the effect of facilitating realization, solving long scheduling time and improving efficiency

Pending Publication Date: 2021-12-03
平安好医投资管理有限公司
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  • Application Information

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Problems solved by technology

[0003] In the process of implementing the present invention, the inventors found that in the prior art, the machine learning model is used in the recognition and processing of medical images, but the operation of the machine learning model needs to consume a lot of computing resources, although the machine learning model is transmitted to Computing is performed on the cloud platform, but this will occupy a large amount of bandwidth resources and cause a long waiting delay, and the processing efficiency of medical images is low

Method used

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  • Medical image processing method and device based on artificial intelligence, equipment and medium
  • Medical image processing method and device based on artificial intelligence, equipment and medium
  • Medical image processing method and device based on artificial intelligence, equipment and medium

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Embodiment 1

[0058] figure 1 It is a flow chart of the artificial intelligence-based medical image processing method provided in Embodiment 1 of the present application. The artificial intelligence-based medical image processing method specifically includes the following steps. According to different requirements, the order of the steps in the flow chart can be changed, and some of them can be omitted.

[0059] S11, in response to a processing instruction for multiple medical images of a target patient, acquire a recognition task corresponding to each medical image.

[0060] Medical images may include medical image information sent by hospital imaging equipment, including but not limited to digital radiography (DR) images, computed tomography (Computed Tomography, CT) images, magnetic resonance (Magnetic Resonance, MR) images, radiation detection (Radiographic testing, RT) images, electrocardiogram (ECG), upper gastrointestinal contrast images, ultrasound images, pathological images, etc....

Embodiment 2

[0122] figure 2 It is a structural diagram of an artificial intelligence-based medical image processing device provided in Embodiment 2 of the present application.

[0123] In some embodiments, the medical image processing device 20 based on artificial intelligence may include a plurality of functional modules composed of computer program segments. The computer program of each program segment in the medical image processing device 20 based on artificial intelligence can be stored in the memory of the electronic device, and executed by at least one processor to execute (see for details figure 1 Describe) the capabilities of artificial intelligence-based medical image processing.

[0124] In this embodiment, the medical image processing device 20 based on artificial intelligence can be divided into multiple functional modules according to the functions it performs. The functional modules may include: a task acquisition module 201 , a factor determination module 202 , a model ...

Embodiment 3

[0188] This embodiment provides a computer-readable storage medium, and a computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps in the above embodiment of the medical image processing method based on artificial intelligence are implemented, for example figure 1 S11-S17 shown:

[0189] S11, in response to a processing instruction for multiple medical images of the target patient, acquire a recognition task corresponding to each medical image;

[0190] S12. Determine an influencing factor of each recognition task according to task information corresponding to the recognition task;

[0191] S13. Determine the hierarchical relationship corresponding to the influencing factor according to the degree of influence of the influencing factor, and construct a hierarchical structure model of the influencing factor according to the hierarchical relationship;

[0192] S14. Obtain a preset judgment matrix, and ca...

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Abstract

The invention relates to the technical field of artificial intelligence, and provides a medical image processing method and device based on artificial intelligence, electronic equipment and a medium. The method comprises the steps: obtaining an identification task corresponding to each medical image in response to a processing instruction for a plurality of medical images of a target patient; determining influence factors of each identification task; according to the hierarchical relationship corresponding to the influence factors, constructing an influence factor hierarchical structure model; obtaining a preset judgment matrix of the image, and calculating a combination weight corresponding to the recognition task based on the judgment matrix and the influence factor hierarchical structure model; generating a task scheduling set of the plurality of identification tasks according to the combined weights corresponding to the identification tasks; sequentially obtaining a target medical image corresponding to each identification task in the task scheduling set in a preset storage space; and calling the AI edge computing device to process the sequentially acquired target medical images. The medical image processing efficiency is improved.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, in particular to an artificial intelligence-based medical image processing method, device, equipment and medium. Background technique [0002] In recent years, with the emergence and development of medical imaging technologies such as magnetic resonance imaging and computed tomography, medical imaging technologies have been widely used in the examination, diagnosis and treatment of various diseases. However, there are still some outstanding problems in the field of medical image diagnosis, for example, the accuracy of recognition based on medical images is low. [0003] In the process of implementing the present invention, the inventors found that in the prior art, the machine learning model is used in the recognition and processing of medical images, but the operation of the machine learning model needs to consume a lot of computing resources, although the machine learn...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H30/20G16H30/40G06K9/32G06K9/62G06N3/04G06N3/08
CPCG16H30/20G16H30/40G06N3/08G06N3/045G06F18/24323G06F18/214
Inventor 高晗李彬
Owner 平安好医投资管理有限公司
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