Focus area classification method and system for full-view digital pathological section

Abstract

The invention discloses a lesion area classification method and system for a full-view digital pathological section. The invention builds a CSResNet system, carries out learning training of the CSResNet system, achieves automatic segmentation of a lesion area in the full-view digital pathological section, classify a segmented lesion area, and judges the type of the lesion area. The residual attention module in the CSResNet system is combined with attention mechanisms in the channel direction and the space direction at the same time, so that the network can transfer the learning gravity centerto a key area, capable of deciding the category of an input image, in a feature map, and high calculation efficiency, classification precision and recognition capability are achieved.

Description

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AI Technical Summary

Benefits of technology

This patented method improves on existing methods by combining two different techniques - one involves identifying specific regions containing abnormalities while another technique uses only those identified region(s). By doing this, models trained from these images have better understanding about how they handle them rather than just focusing their analysis solely upon any single type or location within each pixel's grayscale representation (gray level) alone. Additionally, it suggests setting smaller sizes of pixels around certain parts of an object based on its importance in recognizing important aspects such as disease risk factors. Overall, this approach helps predict diseases faster and accurately without relying heavily on subjective judgment.

Problems solved by technology

The technical problem addressed in this patented text relates how to automatically identify areas where there are abnormalities on histopathology slide sections by analyzing them through image processing techniques like automated microscopy or computers. This can help healthcare professionals make faster diagnoses without having to manually inspect each section individually.

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