mlsanityData to 14 Oct 2026

A Survey of Pathology Foundation Model: Progress and Future Directions

arXiv 2504.04045 ↗First posted 5 Apr 2025cs.CV cs.AI cs.LGcited by 16

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Abstract (arXiv)

Computational pathology, which involves analyzing whole slide images for automated cancer diagnosis, relies on multiple instance learning, where performance depends heavily on the feature extractor and aggregator. Recent Pathology Foundation Models (PFMs), pretrained on large-scale histopathology data, have significantly enhanced both the extractor and aggregator, but they lack a systematic analysis framework. In this survey, we present a hierarchical taxonomy organizing PFMs through a top-down philosophy applicable to foundation model analysis in any domain: model scope, model pretraining, and model design. Additionally, we systematically categorize PFM evaluation tasks into slide-level, patch-level, multimodal, and biological tasks, providing comprehensive benchmarking criteria. Our analysis identifies critical challenges in both PFM development (pathology-specific methodology, end-to-end pretraining, data-model scalability) and utilization (effective adaptation, model maintenance), paving the way for future directions in this promising field. Resources referenced in this survey are available at https://github.com/BearCleverProud/AwesomeWSI.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 46 of its 63 references that are in the map. First posted on arXiv 5 Apr 2025.

Builds on

46 of 63 references.

References · 46

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  11. 2021An Empirical Study of Training Self-Supervised Vision Transformerscited by 1,616
  12. 2022CoCa: Contrastive Captioners are Image-Text Foundation Modelscited by 1,274
34 more
  1. 2021SimMIM: A Simple Framework for Masked Image Modelingcited by 1,153
  2. 2021Perceiver: General Perception with Iterative Attentioncited by 1,104
  3. 2018Attention-based Deep Multiple Instance Learningcited by 1,095
  4. 2023A Comprehensive Survey of Continual Learning: Theory, Method and Applicationcited by 872
  5. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 359
  6. 2022BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizerscited by 303
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  8. 2023LongNet: Scaling Transformers to 1,000,000,000 Tokenscited by 204
  9. 2016Multiple Instance Learning: A Survey of Problem Characteristics and Applicationscited by 164
  10. 2024Multimodal Whole Slide Foundation Model for Pathologycited by 162
  11. 2024Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathologycited by 161
  12. 2022FlexiViT: One Model for All Patch Sizescited by 152
  13. 2021Self-Supervised Representation Learning: Introduction, Advances and Challengescited by 148
  14. 2024PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathologycited by 85
  15. 2021Self-supervised learning methods and applications in medical imaging analysis: A surveycited by 77
  16. 2024Phikon-v2, A large and public feature extractor for biomarker predictioncited by 74
  17. 2024A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Modelcited by 59
  18. 2024Hibou: A Family of Foundational Vision Transformers for Pathologycited by 57
  19. 2025Molecular-driven Foundation Model for Oncologic Pathologycited by 55
  20. 2020Federated Learning for Computational Pathology on Gigapixel Whole Slide Imagescited by 54
  21. 2024Transcriptomics-guided Slide Representation Learning in Computational Pathologycited by 51
  22. 2024RudolfV: A Foundation Model by Pathologists for Pathologistscited by 41
  23. 2024Recent Advances of Multimodal Continual Learning: A Comprehensive Surveycited by 39
  24. 2025Current Pathology Foundation Models are unrobust to Medical Center Differencescited by 39
  25. 2024Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillationcited by 31
  26. 2024Multistain Pretraining for Slide Representation Learning in Pathologycited by 28
  27. 2023Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classificationcited by 27
  28. 2024MoME: Mixture of Multimodal Experts for Cancer Survival Predictioncited by 23
  29. 2024Knowledge-enhanced Pretraining for Vision-language Pathology Foundation Model on Cancer Diagnosiscited by 21
  30. 2024PLUTO: Pathology-Universal Transformercited by 17
  31. 2023PathoDuet: Foundation Models for Pathological Slide Analysis of H&E and IHC Stainscited by 14
  32. 2024A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Modelscited by 9
  33. 2023TAKT: Target-Aware Knowledge Transfer for Whole Slide Image Classificationcited by 7
  34. 2024Pathology Foundation Modelscited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 12.

16 papers.

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  1. 2025Beyond the Failures: Rethinking Foundation Models in Pathologycited by 6
  2. 2025Enhancing Zero-Shot Brain Tumor Subtype Classification via Fine-Grained Patch-Text Alignmentcited by 4
  3. 2026AtlasPatch: Scalable Foundation Model-based Tissue Detection and Patch Extraction for Computational Pathologycited by 3
  4. 2025Exploiting Low-Dimensional Manifold of Features for Few-Shot Whole Slide Image Classificationcited by 1
  5. 2026A protocol for evaluating robustness to H&E staining variation in computational pathology modelscited by 1
  6. 2026Clinically-Informed Modeling for Pediatric Brain Tumor Classification from Whole-Slide Histopathology Imagescited by 1
  7. 2026Benchmarking Pathology Foundation Models for Spatial Domain Understandingcited by 1
  8. 2026BreastGPT: A Multimodal Large Language Model for the Full Spectrum of Breast Cancer Clinical Routinecited by 1
  9. 2025CAPRMIL: Context-Aware Patch Representations for Multiple Instance Learningcited by 0
  10. 2026Investigating the Impact of Histopathological Foundation Models on Regressive Prediction of Homologous Recombination Deficiencycited by 0
  11. 2026BRIGHT: A Collaborative Generalist-Specialist Foundation Model for Breast Pathologycited by 0
  12. 2026Validation of Whole-Slide Foundation Models for Image Retrieval in TCGA Datacited by 0
4 more
  1. 2026DaX: Learning General Pathology Representations Across Scalescited by 0
  2. 2026Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancercited by 0
  3. 2026CellPath-Bench: A Multidimensional Benchmark for Whole-Slide Cellular Representations in Pathology Foundation Modelscited by 0
  4. 2026EXPOSE: Explainable and Domain-Robust Embeddings from Pathology Vision Foundation Models using Sparse Autoencoderscited by 0