Weakly-Supervised Balanced Attention Network for Gastric Pathology Image Localization and Classification

Abstract

Gastric cancer pathological images classification and localization are critical in early diagnosis and therapy of related diseases. Clinically, it takes a long time to scan a pathological image due to its high resolution and blurry boundaries, which leads to requirements for automatic cancer region localization over the pathological image. In this paper, a weakly supervised model is proposed to classify and localize the gastric cancer region in the pathological image with image-level labels. We propose a channel-wise attention (CA) and spatial-wise attention (SA) module to balance the feature (feature balanced module, FBM) and coalesce the dropout attention mechanism (dropout attention module, DAM) into our model to enhance the feature significance. Based on the classification model, we extract the optimal feature map to generate the localization bounding box with a cross attention module. Experiments on a sufficient gastric dataset indicate that our method outperforms other algorithms in classification accuracy and localization accuracy, which demonstrates the effectiveness of our method.