# Patch Transformer for Multi-tagging Whole Slide Histopathology Images

## TL;DR

This paper introduces Patch Transformer, a deep neural network designed for multi-tagging whole slide histopathology images, effectively capturing tag correlations and uniqueness to improve multi-label classification accuracy.

## Contribution

The paper presents a novel end-to-end deep learning model that simultaneously learns patch relations and tag-specific features for multi-label WSI tagging.

## Key findings

- Proven effectiveness on a large dataset of 4,920 WSIs.
- Outperforms existing methods in multi-tagging accuracy.
- Captures tag correlations and individual tag features effectively.

## Abstract

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.