# Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak Labels

arXiv: https://arxiv.org/abs/2210.09021 · HTML: https://mlsanity.com/paper/2210.09021/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 17 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2011.08939/) (2020)
- [A Probabilistic U-Net for Segmentation of Ambiguous Images](https://mlsanity.com/paper/1806.05034/) (2018)
- [Multiple Instance Learning: A Survey of Problem Characteristics and Applications](https://mlsanity.com/paper/1612.03365/) (2016)
- [Weakly supervised multiple instance learning histopathological tumor segmentation](https://mlsanity.com/paper/2004.05024/) (2020)
- [Attention-Based Transformers for Instance Segmentation of Cells in Microstructures](https://mlsanity.com/paper/2011.09763/) (2020)
- [Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach](https://mlsanity.com/paper/1802.02212/) (2018)
- [Learning to Downsample for Segmentation of Ultra-High Resolution Images](https://mlsanity.com/paper/2109.11071/) (2021)
- [OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data](https://mlsanity.com/paper/2110.10640/) (2021)
- [Multiclass Yeast Segmentation in Microstructured Environments with Deep Learning](https://mlsanity.com/paper/2011.08062/) (2020)
- [Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy](https://mlsanity.com/paper/2106.08285/) (2021)

## Built on by (most cited first, 6 of 6)

- [CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classification](https://mlsanity.com/paper/2312.06978/) (2023)
- [Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks](https://mlsanity.com/paper/2407.08625/) (2024)
- [DIOR-ViT: Differential Ordinal Learning Vision Transformer for Cancer Classification in Pathology Images](https://mlsanity.com/paper/2407.08503/) (2024)
- [A Perspective on Deep Vision Performance with Standard Image and Video Codecs](https://mlsanity.com/paper/2404.12330/) (2024)
- [Beyond Labels: A Self-Supervised Framework with Masked Autoencoders and Random Cropping for Breast Cancer Subtype Classification](https://mlsanity.com/paper/2410.12006/) (2024)
- [Transforming H&E images into IHC: A Variance-Penalized GAN for Precision Oncology](https://mlsanity.com/paper/2506.18371/) (2025)

## Abstract (arXiv)

Whole Slide Image (WSI) analysis is a powerful method to facilitate the diagnosis of cancer in tissue samples. Automating this diagnosis poses various issues, most notably caused by the immense image resolution and limited annotations. WSIs commonly exhibit resolutions of 100Kx100K pixels. Annotating cancerous areas in WSIs on the pixel level is prohibitively labor-intensive and requires a high level of expert knowledge. Multiple instance learning (MIL) alleviates the need for expensive pixel-level annotations. In MIL, learning is performed on slide-level labels, in which a pathologist provides information about whether a slide includes cancerous tissue. Here, we propose Self-ViT-MIL, a novel approach for classifying and localizing cancerous areas based on slide-level annotations, eliminating the need for pixel-wise annotated training data. Self-ViT- MIL is pre-trained in a self-supervised setting to learn rich feature representation without relying on any labels. The recent Vision Transformer (ViT) architecture builds the feature extractor of Self-ViT-MIL. For localizing cancerous regions, a MIL aggregator with global attention is utilized. To the best of our knowledge, Self-ViT- MIL is the first approach to introduce self-supervised ViTs in MIL-based WSI analysis tasks. We showcase the effectiveness of our approach on the common Camelyon16 dataset. Self-ViT-MIL surpasses existing state-of-the-art MIL-based approaches in terms of accuracy and area under the curve (AUC).