mlsanityData to 7 Oct 2026

Representation Learning of Histopathology Images using Graph Neural Networks

arXiv 2004.07399 ↗First posted 16 Apr 2020eess.IV cs.CV cs.LGcited by 40

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Abstract (arXiv)

Representation learning for Whole Slide Images (WSIs) is pivotal in developing image-based systems to achieve higher precision in diagnostic pathology. We propose a two-stage framework for WSI representation learning. We sample relevant patches using a color-based method and use graph neural networks to learn relations among sampled patches to aggregate the image information into a single vector representation. We introduce attention via graph pooling to automatically infer patches with higher relevance. We demonstrate the performance of our approach for discriminating two sub-types of lung cancers, Lung Adenocarcinoma (LUAD) & Lung Squamous Cell Carcinoma (LUSC). We collected 1,026 lung cancer WSIs with the 40× magnification from The Cancer Genome Atlas (TCGA) dataset, the largest public repository of histopathology images and achieved state-of-the-art accuracy of 88.8% and AUC of 0.89 on lung cancer sub-type classification by extracting features from a pre-trained DenseNet

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Apr 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 7; 2023: 14; 2024: 4; 2025: 7; 2026: 2.

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  1. 2023Artificial Intelligence for Digital and Computational Pathologycited by 106
  2. 2022A graph-transformer for whole slide image classificationcited by 84
  3. 2021Graph Representation Learning in Biomedicinecited by 79
  4. 2021Hierarchical Graph Representations in Digital Pathologycited by 58
  5. 2022Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunitiescited by 52
  6. 2022Differentiable Zooming for Multiple Instance Learning on Whole-Slide Imagescited by 43
  7. 2021A Survey on Graph-Based Deep Learning for Computational Histopathologycited by 41
  8. 2021HistoCartography: A Toolkit for Graph Analytics in Digital Pathologycited by 20
  9. 2022A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protectioncited by 20
  10. 2024Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structurescited by 20
  11. 2023AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Imagescited by 18
  12. 2023CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Imagescited by 13
28 more
  1. 2024Graph Neural Networks in Histopathology: Emerging Trends and Future Directionscited by 13
  2. 2022Multi stain graph fusion for multimodal integration in pathologycited by 11
  3. 2025Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalancecited by 11
  4. 2022Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applicationscited by 7
  5. 2022Multi-Scale Relational Graph Convolutional Network for Multiple Instance Learning in Histopathology Imagescited by 6
  6. 2023Data efficient deep learning for medical image analysis: A surveycited by 6
  7. 2025Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applicationscited by 6
  8. 2021Pay Attention with Focus: A Novel Learning Scheme for Classification of Whole Slide Imagescited by 5
  9. 2025Fusion of Multi-scale Heterogeneous Pathology Foundation Models for Whole Slide Image Analysiscited by 5
  10. 2021Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flowscited by 4
  11. 2023Context-Aware Self-Supervised Learning of Whole Slide Imagescited by 4
  12. 2023Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathologycited by 3
  13. 2023A Preliminary Investigation into Search and Matching for Tumour Discrimination in WHO Breast Taxonomy Using Deep Networkscited by 3
  14. 2023High-risk Factor Prediction in Lung Cancer Using Thin CT Scans: An Attention-Enhanced Graph Convolutional Network Approachcited by 2
  15. 2023Selection of Distinct Morphologies to Divide & Conquer Gigapixel Pathology Imagescited by 2
  16. 2024A Short Survey on Set-Based Aggregation Techniques for Single-Vector WSI Representation in Digital Pathologycited by 1
  17. 2025BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathologycited by 1
  18. 2023Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classificationcited by 0
  19. 2023MUSTANG: Multi-Stain Self-Attention Graph Multiple Instance Learning Pipeline for Histopathology Whole Slide Imagescited by 0
  20. 2023Classifying Whole Slide Images: What Matters?cited by 0
  21. 2023Global Minima, Recoverability Thresholds, and Higher-Order Structure in GNNScited by 0
  22. 2023Artifact-Robust Graph-Based Learning in Digital Pathologycited by 0
  23. 2024Adversary-Robust Graph-Based Learning of WSIscited by 0
  24. 2025Aggregation Schemes for Single-Vector WSI Representation Learning in Digital Pathologycited by 0
  25. 2025Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtypingcited by 0
  26. 2025Graph Conditioned Diffusion for Controllable Histopathology Image Generationcited by 0
  27. 2026Jigsaw Regularization in Whole-Slide Image Classificationcited by 0
  28. 2026Spatial Blindness in Whole-Slide Multiple Instance Learningcited by 0