mlsanityData to 7 Oct 2026

RudolfV: A Foundation Model by Pathologists for Pathologists

arXiv 2401.04079 ↗First posted 8 Jan 2024eess.IV cs.CV cs.LGcited by 40

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Abstract (arXiv)

Artificial intelligence has started to transform histopathology impacting clinical diagnostics and biomedical research. However, while many computational pathology approaches have been proposed, most current AI models are limited with respect to generalization, application variety, and handling rare diseases. Recent efforts introduced self-supervised foundation models to address these challenges, yet existing approaches do not leverage pathologist knowledge by design. In this study, we present a novel approach to designing foundation models for computational pathology, incorporating pathologist expertise, semi-automated data curation, and a diverse dataset from over 15 laboratories, including 58 tissue types, and encompassing 129 different histochemical and immunohistochemical staining modalities. We demonstrate that our model "RudolfV" surpasses existing state-of-the-art foundation models across different benchmarks focused on tumor microenvironment profiling, biomarker evaluation, and reference case search while exhibiting favorable robustness properties. Our study shows how domain-specific knowledge can increase the efficiency and performance of pathology foundation models and enable novel application areas.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). Builds on 33 of its 72 references that are in the map. First posted on arXiv 8 Jan 2024.

Builds on

33 of 72 references.

References · 33

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2023GPT-4 Technical Reportcited by 18,132
  5. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  6. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  7. 2023Segment Anythingcited by 8,158
  8. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
  9. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  10. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  11. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  12. 2017Prototypical Networks for Few-shot Learningcited by 3,001
21 more
  1. 2020Big Self-Supervised Models are Strong Semi-Supervised Learnerscited by 1,375
  2. 2023Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecturecited by 982
  3. 2021iBOT: Image BERT Pre-Training with Online Tokenizercited by 891
  4. 2023Vision Transformers Need Registerscited by 844
  5. 2018Rotation Equivariant CNNs for Digital Pathologycited by 420
  6. 2019Unmasking Clever Hans Predictors and Assessing What Machines Really Learncited by 359
  7. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 357
  8. 2023Towards Generalist Biomedical AIcited by 286
  9. 2019SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learningcited by 194
  10. 2022Benchmarking Self-Supervised Learning on Diverse Pathology Datasetscited by 148
  11. 2020Self supervised contrastive learning for digital histopathologycited by 146
  12. 2014A Tutorial on Principal Component Analysiscited by 139
  13. 2023Virchow: A Million-Slide Digital Pathology Foundation Modelcited by 86
  14. 2021Divide and Contrast: Self-supervised Learning from Uncurated Datacited by 85
  15. 2021A Petri Dish for Histopathology Image Analysiscited by 63
  16. 2022Self-Supervised Vision Transformers Learn Visual Concepts in Histopathologycited by 49
  17. 2023A General-Purpose Self-Supervised Model for Computational Pathologycited by 42
  18. 2019Yottixel -- An Image Search Engine for Large Archives of Histopathology Whole Slide Imagescited by 41
  19. 2023Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Imagescited by 30
  20. 2023When an Image is Worth 1,024 x 1,024 Words: A Case Study in Computational Pathologycited by 16
  21. 2022Evaluating histopathology transfer learning with ChampKitcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 14; 2025: 15; 2026: 11.

40 papers.

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  1. 2024Multimodal Whole Slide Foundation Model for Pathologycited by 156
  2. 2024Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathologycited by 144
  3. 2024PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathologycited by 80
  4. 2024A Clinical Benchmark of Public Self-Supervised Pathology Foundation Modelscited by 64
  5. 2025Distilling foundation models for robust and efficient models in digital pathologycited by 33
  6. 2024The Clever Hans Effect in Unsupervised Learningcited by 24
  7. 2024Benchmarking Pathology Foundation Models: Adaptation Strategies and Scenarioscited by 16
  8. 2024xMIL: Insightful Explanations for Multiple Instance Learning in Histopathologycited by 15
  9. 2025A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Taskscited by 15
  10. 2025A Survey of Pathology Foundation Model: Progress and Future Directionscited by 14
  11. 2025Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charit'e, and Aignosticscited by 10
  12. 2024A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Modelscited by 9
28 more
  1. 2025SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markerscited by 8
  2. 2025Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Reviewcited by 8
  3. 2024Pathology Foundation Modelscited by 7
  4. 2024EXAONEPath 1.0 Patch-level Foundation Model for Pathologycited by 7
  5. 2025Foundation Models in Medical Imaging: A Review and Outlookcited by 7
  6. 2025MeDi: Metadata-Guided Diffusion Models for Mitigating Biases in Tumor Classificationcited by 6
  7. 2025From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Researchcited by 6
  8. 2026Atlas 2 -- Foundation models for clinical deploymentcited by 6
  9. 2025Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathologycited by 4
  10. 2026MLLM-HWSI: A Multimodal Large Language Model for Hierarchical Whole Slide Image Understandingcited by 3
  11. 2024DiNO-Diffusion. Scaling Medical Diffusion via Self-Supervised Pre-Trainingcited by 2
  12. 2025Benchmarking Foundation Models for Mitotic Figure Classificationcited by 2
  13. 2025PLUTO-4: Frontier Pathology Foundation Modelscited by 2
  14. 2025Foundation Models in Biomedical Imaging: Turning Hype into Realitycited by 2
  15. 2026Distilling Lightweight Domain Experts from Large ML Models by Identifying Relevant Subspacescited by 2
  16. 2026Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathologycited by 2
  17. 2026OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGAcited by 2
  18. 2024Foundational Models for Pathology and Endoscopy Images: Application for Gastric Inflammationcited by 1
  19. 2024DINOv2 Rocks Geological Image Analysis: Classification, Segmentation, and Interpretabilitycited by 1
  20. 2025PhenoBench: A Comprehensive Benchmark for Cell Phenotypingcited by 1
  21. 2026A Comprehensive Benchmark of Histopathology Foundation Models for Kidney Digital Pathology Imagescited by 1
  22. 2024Patherea: Cell Detection and Classification for the 2020scited by 0
  23. 2025Out-of-distribution evaluations of channel agnostic masked autoencoders in fluorescence microscopycited by 0
  24. 2026Mixed Magnification Aggregation for Generalizable Region-Level Representations in Computational Pathologycited by 0
  25. 2026Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracycited by 0
  26. 2026Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generationcited by 0
  27. 2026From Multi-Resolution Cells to Gigapixel Whole Slide Images Foundation Model for Computational Pathologycited by 0
  28. 2026HERO: Histology Encoder for Robust Representation in Oncologycited by 0