Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging
Abstract
Machine-learning models for medical tasks can match or surpass the performance of clinical experts. However, in settings differing from those of the training dataset, the performance of a model can deteriorate substantially. Here we report a representation-learning strategy for machine-learning models applied to medical-imaging tasks that mitigates such 'out of distribution' performance problem and that improves model robustness and training efficiency. The strategy, which we named REMEDIS (for 'Robust and Efficient Medical Imaging with Self-supervision'), combines large-scale supervised transfer learning on natural images and intermediate contrastive self-supervised learning on medical images and requires minimal task-specific customization. We show the utility of REMEDIS in a range of diagnostic-imaging tasks covering six imaging domains and 15 test datasets, and by simulating three realistic out-of-distribution scenarios. REMEDIS improved in-distribution diagnostic accuracies up to 11.5% with respect to strong supervised baseline models, and in out-of-distribution settings required only 1-33% of the data for retraining to match the performance of supervised models retrained using all available data. REMEDIS may accelerate the development lifecycle of machine-learning models for medical imaging.
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Weakly-supervised deep learning models can be trained to perform various downstream computational pathology tasks such as tumor subtyping and prognosis without any region or pixel-level annotations.
“To overcome these limitations, several research groups have alternatively proposed the use of weakly-supervised deep learning models, which can be trained to perform various downstream computational pathology tasks such as tumor subtyping and prognosis32–37 without any region or pixel-level annotations.”
From Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images (2024) · cites this paper as [35]
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 [38]
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 [38]
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 [38]
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 [38]
Foundation models' strong feature representations enable data-efficient applications, requiring fewer labeled samples.
“Their strong feature representations also enable data-efficient applications26,27, requiring fewer labeled samples, which is particularly crucial for medical domains in which expert-annotated data are often limited.”
From A Multimodal Vision Foundation Model for Clinical Dermatology (2025) · cites this paper as [26]
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- Authors
- Azizi, S., Culp, L., Freyberg, J., Mustafa, B., Baur, S., Kornblith, S., Chen, T., Tomasev, N., Mitrović, J., Strachan, P., Mahdavi, S. S., Wulczyn, E., Babenko, B., Walker, M., Loh, A., Chen, P.-H. C., Liu, Y., Bavishi, P., McKinney, S. M., Winkens, J., Roy, A. G., Beaver, Z., Ryan, F., Krogue, J., Etemadi, M., Telang, U., Liu, Y., Peng, L., Corrado, G. S., Webster, D. R., Fleet, D., Hinton, G., Houlsby, N., Karthikesalingam, A., Norouzi, M., & Natarajan, V.
- Affiliations
- Google (United States) (US)
- Google DeepMind (United Kingdom) (GB)
- Georgia Institute of Technology (US)
- Northwestern University (US)
- Corresponding author
- Azizi, Shekoofeh
- Karthikesalingam, Alan
- Natarajan, Vivek
- ORCID
- Azizi, Shekoofeh
- Freyberg, Jan
- Mustafa, Basil
- Baur, Sebastien
- Kornblith, Simon
- Chen, Ting
- Mahdavi, S. Sara
- Babenko, Boris
- Loh, Aaron
- Chen, Po-Hsuan Cameron
- Liu, Yuan
- McKinney, Scott Mayer
- Roy, Abhijit Guha
- Krogue, Justin
- Etemadi, Mozziyar
- Telang, Umesh
- Liu, Yun
- Peng, Lily
- Corrado, Greg S.
- Webster, Dale R.
- Fleet, David
- Karthikesalingam, Alan
- Natarajan, Vivek
- Venue
- Nature Biomedical Engineering
- Published
- 2023-06-08
- Volume
- 7
- Issue
- 6
- Pages
- 756-779
- ISSN
- 2157-846X (ISSN-L)
- OpenAlex
- W4379878309
- Cited by
- 250
- References
- 141
- Citations by year
- 2026: 68 · 2025: 92 · 2024: 77 · 2023: 12 · 2021: 1
- FWCI
- 23.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Cancer Research UK
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- self-supervised learning, representation learning, out-of-distribution generalization, model robustness, transfer learning, contrastive learning, diagnostic imaging, data efficiency
- MeSH terms
- Machine Learning, Supervised Machine Learning, Diagnostic Imaging
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