Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling
Open accessAbstract
Computational pathology is revolutionizing the field of pathology by integrating advanced computer vision and machine learning technologies into diagnostic workflows. It offers unprecedented opportunities for improved efficiency in treatment decisions by allowing pathologists to achieve higher precision and objectivity in disease classification, tumor microenvironment description and identification of new biomarkers. However, the potential of computational pathology in personalized medicine comes with significant challenges, particularly in annotating whole slide images (WSI), which is time-consuming, costly and subject to inter-observer variability. To address these challenges, Self-Supervised Learning (SSL) has emerged as a promising solution to learn representations from histology patches and leverage large volumes of unlabelled WSI. Recently, Masked Image Modeling (MIM) as a SSL framework has emerged and is now considered to outperform purely contrastive learning paradigms. In this work, we therefore explore the application of MIM to histology using iBOT, a self-supervised transformer-based framework. Through a wide range of 17 downstream tasks over seven cancer indications, both at the slide and patch levels, we provide recommendations on the pre-training of large models for histology data using MIM. First, we demonstrate that in-domain pre-training with iBOT outperforms both ImageNet pre-training and a model pre-trained with a purely contrastive learning objective, MoCo v2. Second, we show that Vision Transformers (ViT) models, when scaled appropriately, have the capability to learn pan-cancer representations that benefit a large variety of downstream tasks. Finally, our iBOT ViT-Base model (80 million parameters), pre-trained on more than 40 million histology images from 16 different cancer types, achieves state-of-the-art performance in most weakly-supervised WSI classification tasks compared to other SSL frameworks available in the literature. This paves the way for the development of a foundation model for histopathology. Our code, models and features are publicly available at https://github.com/owkin/HistoSSLscaling .
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SSL techniques such as contrastive learning and masked image modelling have shown improved performance, robustness and higher transferability compared with fully supervised learning.
“SSL techniques such as contrastive learning14,15 and masked image modelling16 have shown improved performance, robustness and higher transferability compared with fully supervised learning.”
From Benchmarking foundation models as feature extractors for weakly supervised computational pathology (2025) · cites this paper as [16]
Virchow is the largest foundation model for computational pathology to date.
“we present Virchow, the largest foundation model for computational pathology to date.”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [37]
These works demonstrate that image features produced with self-supervised learning of pathology images outperform image features trained on natural images and that performance improves with scale.
“These works demonstrate that image features produced with self-supervised learning of pathology images outperform image features trained on natural images and that performance improves with scale.”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [37]
Recent pioneering works have utilized datasets ranging from 30,000 to 400,000 WSIs to train foundation models ranging in size from 28 million to 307 million parameters.
“Despite the challenges in collecting large-scale datasets in the pathology domain, recent pioneering works have utilized datasets ranging from 30,000 to 400,000 WSIs to train foundation models ranging in size from 28 million to 307 million parameters35–42”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [37]
Self-supervised learning is enabling in-domain transfer by removing the label requirement, driving a second wave of scaling to tens of thousands of WSIs.
“Now self-supervised learning is enabling in-domain transfer by removing the label requirement, driving a second wave of scaling to tens of thousands of WSIs to inform image representation35–39,56.”
From A foundation model for clinical-grade computational pathology and rare cancers detection (2024) · cites this paper as [37]
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Jaume, G., Oldenburg, L., Vaidya, A., Chen, R. J., Williamson, D. F. K., Peeters, T., Song, A. H., & Mahmood, F. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.00920 · Cited by 51 · see citing works
Multistain Pretraining for Slide Representation Learning in Pathology
Jaume, G., Vaidya, A., Zhang, A., H. Song, A., J. Chen, R., Sahai, S., Mo, D., Madrigal, E., Phi Le, L., & Mahmood, F. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-73414-4_2 · Cited by 27 · see citing works
Hibou: A Family of Foundational Vision Transformers for Pathology
Nechaev, D., Pchelnikov, A., & Ivanova, E. (2024) · arXiv · doi:10.48550/arxiv.2406.05074 · Cited by 18 · see citing works
Distilling Foundation Models for Robust and Efficient Models in Digital Pathology
Filiot, A., Dop, N., Tchita, O., Riou, A., Dubois, R., Peeters, T., Valter, D., Scalbert, M., Saillard, C., Robin, G., et al. (2026) · Lecture Notes in Computer Science · doi:10.1007/978-3-032-04981-0_16 · Cited by 17 · see citing works
Self-citation
- Authors
- Filiot, A., Ghermi, R., Olivier, A., Jacob, P., Fidon, L., Camara, A., Mac Kain, A., Saillard, C., & Schiratti, J.-B.
- Corresponding author
- Filiot, Alexandre
- ORCID
- Venue
- medRxiv
- Publisher
- openRxiv
- Published
- 2023-07-26
- OpenAlex
- W4385285835
- Semantic Scholar
- c6e6946db4488c6696978438809f1317de2061f7
- Cited by
- 148
- References
- 74
- Citations by year
- 2026: 36 · 2025: 67 · 2024: 40 · 2023: 5
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- medRxiv (acceptedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- self-supervised learning, masked image modeling, histopathology, whole slide images, vision transformer, weakly supervised classification, foundation models, cancer diagnosis
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