ArticleNature biomedical engineering2023
Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging.
Article in Nature biomedical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 115 papers, 1 of them a synthesis that pooled it.
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Who cites it
115 citing papers in PubMed, 1 synthesis or guideline pooled it, 215 citations in OpenAlex.
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- Learning neuroimaging models from health system-scale data.Nature biomedical engineering · 2026Article
- Diagnostic accuracy of image-based deep learning for glaucomatous optic neuropathy detection: a systematic review and meta-analysis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
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- Machine Learning for Radiomics in Oncology: Challenges, Limitations, and Future Directions.Sensors (Basel, Switzerland) · 2026Article
- A paradigm of hybrid-supervision for annotation-scarce periapical film analysis.BMC oral health · 2026Article
- Autodidactic dense anatomical models.Medical image analysis · 2026Article
- SULBA: A Task-Agnostic Data Augmentation Framework for Deep Learning in Medical Image Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Dissecting self-supervised learning strategies for transfer learning in MRI prostate cancer diagnosis.Scientific reports · 2026Article
- Deep learning-based prediction of acute pancreatitis severity from abdominal CT with multicenter external validation.Radiology advances · 2026Article
- A generalist foundation model and database for open-world medical image segmentation.Nature biomedical engineering · 2026Article
- Multimodal prediction of visual improvement in diabetic macular edema using real-world electronic health records and optical coherence tomography images.medRxiv : the preprint server for health sciences · 2026Article
- 3D foundation model for generalizable disease detection in head computed tomography.Nature biomedical engineering · 2026Article
- Leveraging Ensemble on Self-Supervised Techniques to Enhance Model Performance: A Regime Analysis.Journal of imaging informatics in medicine · 2026Article
55 more citing papers are in PubMed but not listed here.
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Authors and funding
36 authors at 4 institutions in 2 countries.
Funding
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.