mlsanityData to 7 Oct 2026

Benchmarking Self-Supervised Learning on Diverse Pathology Datasets

arXiv 2212.04690 ↗First posted 9 Dec 2022cs.CV cs.LGcited by 148

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Abstract (arXiv)

Computational pathology can lead to saving human lives, but models are annotation hungry and pathology images are notoriously expensive to annotate. Self-supervised learning has shown to be an effective method for utilizing unlabeled data, and its application to pathology could greatly benefit its downstream tasks. Yet, there are no principled studies that compare SSL methods and discuss how to adapt them for pathology. To address this need, we execute the largest-scale study of SSL pre-training on pathology image data, to date. Our study is conducted using 4 representative SSL methods on diverse downstream tasks. We establish that large-scale domain-aligned pre-training in pathology consistently out-performs ImageNet pre-training in standard SSL settings such as linear and fine-tuning evaluations, as well as in low-label regimes. Moreover, we propose a set of domain-specific techniques that we experimentally show leads to a performance boost. Lastly, for the first time, we apply SSL to the challenging task of nuclei instance segmentation and show large and consistent performance improvements under diverse settings.

Key facts. Cited by 148 papers in the map (data through 7 Oct 2026). Builds on 46 of its 72 references that are in the map. First posted on arXiv 9 Dec 2022.

Builds on

46 of 72 references.

References · 46

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34 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 22; 2024: 64; 2025: 39; 2026: 23.

148 papers.

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  1. 2024Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathologycited by 144
  2. 2024HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysiscited by 104
  3. 2023Virchow: A Million-Slide Digital Pathology Foundation Modelcited by 86
  4. 2024PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathologycited by 80
  5. 2024Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathologycited by 70
  6. 2024Phikon-v2, A large and public feature extractor for biomarker predictioncited by 67
  7. 2024Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysiscited by 66
  8. 2024A Clinical Benchmark of Public Self-Supervised Pathology Foundation Modelscited by 64
  9. 2023Attention-Challenging Multiple Instance Learning for Whole Slide Image Classificationcited by 61
  10. 2024Hibou: A Family of Foundational Vision Transformers for Pathologycited by 52
  11. 2024Transcriptomics-guided Slide Representation Learning in Computational Pathologycited by 50
  12. 2023A General-Purpose Self-Supervised Model for Computational Pathologycited by 42
38 more
  1. 2024RudolfV: A Foundation Model by Pathologists for Pathologistscited by 40
  2. 2024In-context learning enables multimodal large language models to classify cancer pathology imagescited by 38
  3. 2023Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learningcited by 34
  4. 2024CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathologycited by 34
  5. 2023WsiCaption: Multiple Instance Generation of Pathology Reports for Gigapixel Whole-Slide Imagescited by 31
  6. 2023Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Imagescited by 30
  7. 2023Domain Generalization in Computational Pathology: Survey and Guidelinescited by 29
  8. 2024Data-Centric Foundation Models in Computational Healthcare: A Surveycited by 27
  9. 2024Multistain Pretraining for Slide Representation Learning in Pathologycited by 27
  10. 2024Knowledge-enhanced Pretraining for Vision-language Pathology Foundation Model on Cancer Diagnosiscited by 21
  11. 2024CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignmentcited by 20
  12. 2023A Foundational Multimodal Vision Language AI Assistant for Human Pathologycited by 19
  13. 2024Vim4Path: Self-Supervised Vision Mamba for Histopathology Imagescited by 19
  14. 2024Towards Large-Scale Training of Pathology Foundation Modelscited by 19
  15. 2023Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classificationcited by 18
  16. 2024PLUTO: Pathology-Universal Transformercited by 17
  17. 2024Benchmarking Pathology Foundation Models: Adaptation Strategies and Scenarioscited by 16
  18. 2023Rotation-Agnostic Image Representation Learning for Digital Pathologycited by 15
  19. 2024Unsupervised Foundation Model-Agnostic Slide-Level Representation Learningcited by 15
  20. 2025A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Taskscited by 15
  21. 2023Self-supervised Representation Learning From Random Data Projectorscited by 14
  22. 2024Knowledge-enhanced Visual-Language Pretraining for Computational Pathologycited by 14
  23. 2024Learning biologically relevant features in a pathology foundation model using sparse autoencoderscited by 14
  24. 2024Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysiscited by 12
  25. 2023Domain-specific optimization and diverse evaluation of self-supervised models for histopathologycited by 11
  26. 2024AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image Classificationcited by 10
  27. 2024Sm: enhanced localization in Multiple Instance Learning for medical imaging classificationcited by 10
  28. 2025Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charit'e, and Aignosticscited by 10
  29. 2024A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Modelscited by 9
  30. 2023DPA-P2PNet: Deformable Proposal-aware P2PNet for Accurate Point-based Cell Detectioncited by 8
  31. 2023Improving Representation Learning for Histopathologic Images with Cluster Constraintscited by 8
  32. 2024SCMIL: Sparse Context-aware Multiple Instance Learning for Predicting Cancer Survival Probability Distribution in Whole Slide Imagescited by 8
  33. 2024Are We Ready for Out-of-Distribution Detection in Digital Pathology?cited by 8
  34. 2025SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markerscited by 8
  35. 2023When is a Foundation Model a Foundation Modelcited by 7
  36. 2024RetMIL: Retentive Multiple Instance Learning for Histopathological Whole Slide Image Classificationcited by 7
  37. 2024Pathology Foundation Modelscited by 7
  38. 2024EXAONEPath 1.0 Patch-level Foundation Model for Pathologycited by 7

50 most cited of 148 All 148 (JSON)