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Cited by 250Domain Adaptation and Few-Shot LearningArtificial Intelligence in Healthcare and EducationMedical Image Segmentation Techniques

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Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging

Azizi, Shekoofeh, Culp, Laura, Freyberg, Jan, Mustafa, Basil, Baur, Sebastien, Kornblith, Simon, Chen, Ting, Tomasev, Nenad, Mitrović, Jovana, Strachan, Patricia, Mahdavi, S. Sara, Wulczyn, Ellery, Babenko, Boris, Walker, Megan, Loh, Aaron, Chen, Po-Hsuan Cameron, Liu, Yuan, Bavishi, Pinal, McKinney, Scott Mayer, Winkens, Jim, Roy, Abhijit Guha, Beaver, Zach, Ryan, Fiona, Krogue, Justin, Etemadi, Mozziyar, Telang, Umesh, Liu, Yun, Peng, Lily, Corrado, Greg S., Webster, Dale R., Fleet, David, Hinton, Geoffrey, Houlsby, Neil, Karthikesalingam, Alan, Norouzi, Mohammad, Natarajan, Vivek·2023·Nature Biomedical Engineering
DOI 10.1038/s41551-023-01049-7
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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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Metadata

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
Venue
Nature Biomedical Engineering
Publisher
Springer Science and Business Media LLC
Published
2023-06-08
Volume
7
Issue
6
Pages
756-779
ISSN
2157-846X (ISSN-L)
DOI
10.1038/s41551-023-01049-7
OpenAlex
W4379878309
Cited by
250
References
141
Citations by year
2026: 68 · 2025: 92 · 2024: 77 · 2023: 12
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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