Applications for access to the Optimam database can be made using this web form. The datasets from Northwestern Medicine and Apollo Hospitals were used under a licence for the current study and are not publicly available. REMEDIS may accelerate the development lifecycle of machine-learning models for medical imaging. 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. 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. 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. 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. However, in settings differing from those of the training dataset, the performance of a model can deteriorate substantially.
Machine-learning models for medical tasks can match or surpass the performance of clinical experts. Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging
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