End-to-End Attention Pooling-Based Classification Method for Histopathology Images

Abstract

The present disclosure provides an end-to-end attention pooling-based classification method for histopathological images. The method specifically includes the following steps: S1, cutting the histopathology image into patches of a specified size, removing the patches with too much background area and packaging the remaining patches into a bag; S2, training a deep learning network by taking the bag obtained in S1 as an input using a standard multi-instance learning method; S3, scoring all the patches by using the trained deep learning network, and selecting m patches with highest and lowest scores for each whole slide image to form a new bag; S4, building a deep learning network including an attention pooling module, and training the network by using the new bag obtained in S3; and S5, after the histopathology image to be classified is processed in S1 and S3, performing classification by using the model obtained in S4. The present disclosure can obtain a better classification effect under the current situation of only a small number of samples, provide an auxiliary diagnosis mechanism for doctors, and alleviate the problem of shortage of medical resources.

Dependencies are inferred from the claim text.