# Artifact-Robust Graph-Based Learning in Digital Pathology

arXiv: https://arxiv.org/abs/2310.18192 · HTML: https://mlsanity.com/paper/2310.18192/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 54 references that are in the map.
- First posted on arXiv 27 Oct 2023.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Ensemble Adversarial Training: Attacks and Defenses](https://mlsanity.com/paper/1705.07204/) (2017)
- [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)
- [Adversarial Attacks on Neural Networks for Graph Data](https://mlsanity.com/paper/1805.07984/) (2018)
- [Compressed Sensing using Generative Models](https://mlsanity.com/paper/1703.03208/) (2017)
- [Revisiting Graph Neural Networks: All We Have is Low-Pass Filters](https://mlsanity.com/paper/1905.09550/) (2019)
- [Understanding Robustness of Transformers for Image Classification](https://mlsanity.com/paper/2103.14586/) (2021)
- [Learning to Drop: Robust Graph Neural Network via Topological Denoising](https://mlsanity.com/paper/2011.07057/) (2020)
- [Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks](https://mlsanity.com/paper/2107.13048/) (2021)
- [DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks](https://mlsanity.com/paper/2111.06283/) (2021)
- [A graph-transformer for whole slide image classification](https://mlsanity.com/paper/2205.09671/) (2022)
- [Representation Learning of Histopathology Images using Graph Neural Networks](https://mlsanity.com/paper/2004.07399/) (2020)
- [Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology](https://mlsanity.com/paper/2206.14973/) (2022)
- [Histographs: Graphs in Histopathology](https://mlsanity.com/paper/1908.05020/) (2019)
- [Untrained Graph Neural Networks for Denoising](https://mlsanity.com/paper/2109.11700/) (2021)
- [From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology](https://mlsanity.com/paper/2204.05044/) (2022)
- [Now You See It, Now You Dont: Adversarial Vulnerabilities in Computational Pathology](https://mlsanity.com/paper/2106.08153/) (2021)
- [Context-Aware Self-Supervised Learning of Whole Slide Images](https://mlsanity.com/paper/2306.04763/) (2023)
- [Reliable Graph Neural Network Explanations Through Adversarial Training](https://mlsanity.com/paper/2106.13427/) (2021)

## Abstract (arXiv)

Whole slide images~(WSIs) are digitized images of tissues placed in glass slides using advanced scanners. The digital processing of WSIs is challenging as they are gigapixel images and stored in multi-resolution format. A common challenge with WSIs is that perturbations/artifacts are inevitable during storing the glass slides and digitizing them. These perturbations include motion, which often arises from slide movement during placement, and changes in hue and brightness due to variations in staining chemicals and the quality of digitizing scanners. In this work, a novel robust learning approach to account for these artifacts is presented. Due to the size and resolution of WSIs and to account for neighborhood information, graph-based methods are called for. We use graph convolutional network~(GCN) to extract features from the graph representing WSI. Through a denoiser {and pooling layer}, the effects of perturbations in WSIs are controlled and the output is followed by a transformer for the classification of different grades of prostate cancer. To compare the efficacy of the proposed approach, the model without denoiser is trained and tested with WSIs without any perturbation and then different perturbations are introduced in WSIs and passed through the network with the denoiser. The accuracy and kappa scores of the proposed model with prostate cancer dataset compared with non-robust algorithms show significant improvement in cancer diagnosis.