  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.
