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Adversary-Robust Graph-Based Learning of WSIs

arXiv 2403.14489 ↗First posted 21 Mar 2024cs.CVcited by 0

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Abstract (arXiv)

Enhancing the robustness of deep learning models against adversarial attacks is crucial, especially in critical domains like healthcare where significant financial interests heighten the risk of such attacks. Whole slide images (WSIs) are high-resolution, digitized versions of tissue samples mounted on glass slides, scanned using sophisticated imaging equipment. The digital analysis of WSIs presents unique challenges due to their gigapixel size and multi-resolution storage format. In this work, we aim at improving the robustness of cancer Gleason grading classification systems against adversarial attacks, addressing challenges at both the image and graph levels. As regards the proposed algorithm, we develop a novel and innovative graph-based model which utilizes GNN to extract features from the graph representation of WSIs. A denoising module, along with a pooling layer is incorporated to manage the impact of adversarial attacks on the WSIs. The process concludes with a transformer module that classifies various grades of prostate cancer based on the processed data. To assess the effectiveness of the proposed method, we conducted a comparative analysis using two scenarios. Initially, we trained and tested the model without the denoiser using WSIs that had not been exposed to any attack. We then introduced a range of attacks at either the image or graph level and processed them through the proposed network. The performance of the model was evaluated in terms of accuracy and kappa scores. The results from this comparison showed a significant improvement in cancer diagnosis accuracy, highlighting the robustness and efficiency of the proposed method in handling adversarial challenges in the context of medical imaging.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 34 of its 58 references that are in the map. First posted on arXiv 21 Mar 2024.

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References · 34

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2017Graph Attention Networkscited by 8,923
  4. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  5. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  6. 2013Intriguing properties of neural networkscited by 5,155
  7. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  8. 2017Ensemble Adversarial Training: Attacks and Defensescited by 840
  9. 2016Learning to learn by gradient descent by gradient descentcited by 620
  10. 2019Graph Transformer Networkscited by 523
  11. 2016Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samplescited by 446
  12. 2018Adversarial Attacks on Neural Networks for Graph Datacited by 397
22 more
  1. 2017Compressed Sensing using Generative Modelscited by 339
  2. 2018Adversarial Attack on Graph Structured Datacited by 301
  3. 2019Adversarial Attacks on Graph Neural Networks via Meta Learningcited by 261
  4. 2021Understanding Robustness of Transformers for Image Classificationcited by 249
  5. 2019Topology Attack and Defense for Graph Neural Networks: An Optimization Perspectivecited by 196
  6. 2020Learning to Drop: Robust Graph Neural Network via Topological Denoisingcited by 127
  7. 2022A graph-transformer for whole slide image classificationcited by 84
  8. 2018Fast Gradient Attack on Network Embeddingcited by 65
  9. 2018Adversarial Attacks Against Medical Deep Learning Systemscited by 62
  10. 2019Transferring Robustness for Graph Neural Network Against Poisoning Attackscited by 58
  11. 2020Representation Learning of Histopathology Images using Graph Neural Networkscited by 40
  12. 2020A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learningcited by 35
  13. 2019Attacking Graph Convolutional Networks via Rewiringcited by 32
  14. 2019A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learningcited by 31
  15. 2022Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathologycited by 26
  16. 2019Histographs: Graphs in Histopathologycited by 20
  17. 2020Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Modelscited by 18
  18. 2021Untrained Graph Neural Networks for Denoisingcited by 15
  19. 2022From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathologycited by 9
  20. 2021Now You See It, Now You Dont: Adversarial Vulnerabilities in Computational Pathologycited by 7
  21. 2023Context-Aware Self-Supervised Learning of Whole Slide Imagescited by 4
  22. 2021Reliable Graph Neural Network Explanations Through Adversarial Trainingcited by 3

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