Patch Transformer for Multi-tagging Whole Slide Histopathology Images
This addresses the growing demand for automated multi-tagging in histopathology, which is incremental as it extends existing single-tag methods to handle multiple tags.
The authors tackled the problem of automated multi-tagging for whole slide histopathology images, proposing a Patch Transformer model that predicts multiple slide-level tags by learning patch characteristics and tag-wise uniqueness, achieving effectiveness demonstrated on a dataset of 4,920 WSIs.
Automated whole slide image (WSI) tagging has become a growing demand due to the increasing volume and diversity of WSIs collected nowadays in histopathology. Various methods have been studied to classify WSIs with single tags but none of them focuses on labeling WSIs with multiple tags. To this end, we propose a novel end-to-end trainable deep neural network named Patch Transformer which can effectively predict multiple slide-level tags from WSI patches based on both the correlations and the uniqueness between the tags. Specifically, the proposed method learns patch characteristics considering 1) patch-wise relations through a patch transformation module and 2) tag-wise uniqueness for each tagging task through a multi-tag attention module. Extensive experiments on a large and diverse dataset consisting of 4,920 WSIs prove the effectiveness of the proposed model.