Automatic grading method for diabetic retinopathy image based on label coding

An automatic grading and lesion level technology, applied in medical images, medical data mining, instruments, etc., can solve problems such as lack of generalization ability

Active Publication Date: 2021-02-05
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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The disadvantage of this algorithm is that it can only convert hard label...

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  • Automatic grading method for diabetic retinopathy image based on label coding
  • Automatic grading method for diabetic retinopathy image based on label coding
  • Automatic grading method for diabetic retinopathy image based on label coding

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[0050]The present invention will be further described in detail below in combination with test examples and specific implementations. However, it should not be understood that the scope of the above-mentioned subject of the present invention is limited to the following embodiments, and all technologies implemented based on the content of the present invention belong to the scope of the present invention.

[0051]The technical problem to be solved by the present invention is to provide a label coding method, which enables the deep model to learn the expected probability distribution of the category in the classification of diabetes-induced retinopathy, so as to improve the accuracy and effectiveness of the classification. The entire algorithm design process is as followsFigure 4Shown, including steps:

[0052]Step 1.1: The five grades of glyconet disease are normal, mild, moderate, severe and value-added, and the corresponding hard tag i is set to 0, 1, 2, 3, and 4 respectively. For any ha...

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Abstract

The invention discloses an automatic grading method for a diabetic retinopathy image based on label coding, and the label coding technology not only can be used for grading the diabetic retinopathy image, but also can be applied to other orderly classification problems. According to the method, firstly, soft coding is carried out on five lesion grade labels of the diabetic retinopathy images to control model prediction probability distribution so as to solve the class dependence problem among the labels; a diabetic retinopathy data set is built and model parameters and offset parameters in theparameter soft labels are obtained through model training; soft codes of five lesion levels are calculated by utilizing the offset parameters, wherein the soft codes can be used for transfer learning; and finally, accurately the diabetic retinopathy images are graded accurately through the model obtained by training. The method can effectively eliminate the dependence between labels in the diabetic retinopathy grading problem, the label prediction probability distribution of the model can be flexibly controlled through the soft coding method, and the accuracy of diabetic retinopathy grading is improved; and meanwhile, hard and soft label mapping can be established for transfer learning.

Description

Technical field[0001]The present invention relates to the application field of image processing and machine vision, especially the severity assessment and intelligent grading of diabetic-induced retinopathy. The label coding technology in the present invention is also applicable to other ordered classification problems in deep learning.Background technique[0002]In the severity classification of diabetes-induced retinopathy, according to the International Clinical Diabetic Retinopathy (DR) Severity Scale, as shown in Table 1, the severity of the disease can be divided into five categories: normal, mild, moderate, severe, and value-added, such asfigure 1 Shown. Part of the classification of the severity of the lesion is an ordered classification problem, and there is a dependency relationship between each category. For a certain label, different categories play different roles in predicting probability distribution. For example, different predictions for value-added (DR4) fundus photo...

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Application Information

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IPC IPC(8): G16H30/40G16H50/70G06N20/00
CPCG16H30/40G16H50/70G06N20/00
Inventor 邓佳坤彭真明朱强孙晓丽魏浩然程晓彬赵学功唐普英
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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