Focus segmentation method and device and medium

Abstract

The invention discloses a focus segmentation method and device and a medium, and belongs to the field of computer vision, and the method comprises a global attention module based on a transformer structure, a feature attention module based on the transformer structure, and the use of a cross entropy loss function with a weight based on the above modules. According to the method, detail information of the lesions and the blood vessels can be obtained, rich fundus prior information contained in blood vessel distribution can be fully utilized, the dependency relationship of different lesions in the image and the recessive pathological association between the lesions and the blood vessels can be obtained, and simultaneous segmentation of the DR multiple lesions can be more accurately achieved; the problem that foreground and background pixel points are unbalanced is solved, simultaneous accurate segmentation of multiple difficult and important lesions in actual medical diagnosis is achieved, and the technical problems that in the prior art, a segmentation model is difficult to learn standard features of the lesions, and wrong segmentation exists are solved.

Description

technical field [0001] The present invention relates to the field of computer vision, and more specifically, to a method, device and medium for lesion segmentation. Background technique [0002] The existing technical solutions for lesion segmentation have the following technical problems: 1. There are large differences between the same type of lesions in different disease development stages, making it difficult for the segmentation model to learn the standard features of lesions; 2. There are morphological differences between different lesions. A certain degree of similarity, such as hard exudate and soft exudate, makes the model appear mis-segmentation and mis-segmentation. Diabetic retinopathy (DR) will be described as an example below, but it is not limited to diabetes. [0003] Diabetic retinopathy (DR) is a complication caused by multiple long-duration diabetes. my country is the country with the largest number of diabetic patients in the world. The prevalence rate of...

AI Technical Summary

Problems solved by technology

[0002] The existing technical solutions for lesion segmentation have the following technical problems: 1. There are large differences between the same type of lesions in different disease development stages, making it difficult for the segmentation model to learn the standard features of lesions; 2. There are morphological differences between different lesions. A certain degree of similarity, such as hard exudate and soft exudate, makes the model mis-segment and mis-segment

However, the shortage of professional ophthalmologists and the poor medical environment in remote areas have seriously restricted the process of screening and diagnosis, and manual real-time analysis is even more difficult

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