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Deep learning models for semantic segmentation of images require largeamounts of data. In the medical imaging domain, acquiring sufficient data is asignificant challenge. Labeling medical image data requires expert knowledge.Collaboration between institutions could address this challenge, but sharingmedical data to a centralized location faces various legal, privacy, technical,and data-ownership challenges, especially among international institutions. Inthis study, we introduce the first use of federated learning formulti-institutional collaboration, enabling deep learning modeling withoutsharing patient data. Our quantitative results demonstrate that the performanceof federated semantic segmentation models (Dice=0.852) on multimodal brainscans is similar to that of models trained by sharing data (Dice=0.862). Wecompare federated learning with two alternative collaborative learning methodsand find that they fail to match the performance of federated learning.

Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation