A privacy-preserving federated learning infrastructure for prostate segmentation on T2-Weighted MRI
Fadila Zerka1, Mohammed Sunoqrot1, Bendik Abrahamsen1, Alexandros Patsanis1, Tone Frost Bathen1,2, and Mattijs Elschot1,2
1Department of Circulation and Medical Imaging, NTNU, Trondheim, Norway, 2Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim, Norway
Accessing medical data is highly protected by law and ethics, making data sharing difficult and time-consuming. Distributed learning in its various forms allows learning from medical data without these data ever leaving the medical institutions. In this study, we evaluate the Flower federated learning framework for prostate segmentation on T2-Weighted MRI. The results show that the Federated learning framework performs comparably to the reference (centralized learning) model.
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