# Patch Transformer for Multi-tagging Whole Slide Histopathology Images

arXiv: https://arxiv.org/abs/1906.04151 · HTML: https://mlsanity.com/paper/1906.04151/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2019.

## Built on by (most cited first, 13 of 13)

- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Differentiable Patch Selection for Image Recognition](https://mlsanity.com/paper/2104.03059/) (2021)
- [Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review](https://mlsanity.com/paper/2301.03505/) (2023)
- [Vision Transformers in Medical Computer Vision -- A Contemplative Retrospection](https://mlsanity.com/paper/2203.15269/) (2022)
- [A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers](https://mlsanity.com/paper/2104.06243/) (2021)
- [A self-supervised framework for learning whole slide representations](https://mlsanity.com/paper/2402.06188/) (2024)
- [Multi-level Multiple Instance Learning with Transformer for Whole Slide Image Classification](https://mlsanity.com/paper/2306.05029/) (2023)
- [Towards Hierarchical Regional Transformer-based Multiple Instance Learning](https://mlsanity.com/paper/2308.12634/) (2023)
- [Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classification](https://mlsanity.com/paper/2305.02032/) (2023)
- [Unleashing the Infinity Power of Geometry: A Novel Geometry-Aware Transformer (GOAT) for Whole Slide Histopathology Image Analysis](https://mlsanity.com/paper/2402.05373/) (2024)
- [Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Auto-Encoders](https://mlsanity.com/paper/2203.10645/) (2022)
- [Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones](https://mlsanity.com/paper/2207.13700/) (2022)
- [Multiplex-detection Based Multiple Instance Learning Network for Whole Slide Image Classification](https://mlsanity.com/paper/2208.03526/) (2022)

## Abstract (arXiv)

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.