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Rare skin lesion classification system based on spatial transformation optimization meta learning

A technology of spatial transformation and skin lesions, applied in the field of rare skin lesion classification system, can solve the problems of lack of skin lesion image data, wrong referral, nursing delay, etc. Effect

Pending Publication Date: 2022-01-07
SHANDONG NORMAL UNIV
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Problems solved by technology

Due to a shortage of dermatologists, many patients can only be seen by GPs, which can lead to misreferrals, delays in care, and errors in diagnosis and treatment
[0004] As a leading artificial intelligence method, deep learning is very effective in solving skin lesion diagnosis tasks, but in reality there is a major problem of lack of medical training sample skin lesion image data

Method used

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  • Rare skin lesion classification system based on spatial transformation optimization meta learning
  • Rare skin lesion classification system based on spatial transformation optimization meta learning
  • Rare skin lesion classification system based on spatial transformation optimization meta learning

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Embodiment Construction

[0021] It should be noted that the following detailed description is exemplary and intended to provide further explanation of the present invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0022] Such as Figure 5 As shown, a rare skin lesion classification system based on spatial transformation optimization meta-learning, including:

[0023] An acquisition module configured to: acquire skin images to be classified;

[0024] The classification module is configured to: process the skin image to be classified according to the trained meta-learning model based on space transformation optimization, and obtain the skin lesion classification result.

[0025] Further, the meta-learning model based on space conversion optimization; specifically includes:

[0026] The first spatial transformation network, the first convolution module, t...

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Abstract

The invention discloses a rare skin lesion classification system based on spatial transformation optimization meta learning, and the system comprises an obtaining module which is configured to obtain a to-be-classified skin image; and a classification module which is configured to process the to-be-classified skin image according to the trained meta learning model based on spatial conversion optimization to obtain a skin lesion classification result. The problem of recognizing diseases from skin injury images in a low data state is formulated into a simple learning problem, model-independent meta-learning is applied, the ability of a system to quickly adapt to new tasks and environments is absorbed, only little training is needed, and the influence caused by insufficient sample data is relieved.

Description

technical field [0001] The invention relates to the technical field of medical image processing, in particular to a classification system for rare skin lesions based on meta-learning for space transformation optimization. Background technique [0002] The statements in this section merely mention the background technology related to the present invention and do not necessarily constitute the prior art. [0003] Skin cancer is the most common cancer worldwide and melanoma is the deadliest cancer. With a shortage of dermatologists, many patients can only be seen by general practitioners, which can lead to misreferrals, delays in care, and errors in diagnosis and treatment. [0004] As a leading artificial intelligence method, deep learning is very effective in solving skin lesion diagnosis tasks, but in reality there is a major problem of lack of medical training sample skin lesion image data. Contents of the invention [0005] In order to solve the deficiencies of the pri...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06V10/774G06V10/764G06N3/04
CPCG06T7/0012G06T2207/30096G06T2207/30088G06N3/045G06F18/241G06F18/214
Inventor 李登旺高祝敏黄浦董雪媛洪亭轩田伟伟王建波朱慧李婕吴冰柴象飞章桦
Owner SHANDONG NORMAL UNIV