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Cancer Research Center, University of Heidelberg
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A new open-source framework transforms how multi-center radiology studies monitor progress and explore data, enabling real-time insights and efficient collaboration.
Real-world label noise can significantly hinder federated learning in medical image segmentation, but our benchmark suite offers a comprehensive solution for evaluating FNLL methods under realistic conditions.
