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Achieving competitive medical image segmentation with just 10 annotated cases could revolutionize the efficiency of clinical workflows.
Simple global intensity nonlinear (GIN) augmentation enables surprisingly effective cross-modality generalization in federated medical image segmentation, outperforming complex alternatives and closing the gap with centralized training.
