A Context-Guided Attention Method for Integrating Features of Histopathological Patches

Abstract

Lots of researchers have studied for classifying histopathological whole slide images (WSIs). Since a WSI is too large to be processed directly, researchers usually cut it into many small-sized patches and then integrate the discriminative features extracted from the patches to obtain a slide-level feature of the WSI. The integration strategy generating the slide-level features is crucial for the WSI classification model. Lots of attention-based methods have been proposed for such purpose. However, most attention-based methods do not take the patches relationship into consideration, which affects the classification performance of the models. In this work, we propose a novel Context-Guided attention (CGattention) method to integrate the patch-level features, which constructs a context vector to simulate the global context information of the whole WSI and implicitly characterizes the relationship between patches in the WSI. When evaluated on two publicly available datasets, the CGattention based model obtained the better performance than other attention-based models.