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Early prostate cancer detection and staging from MRI are extremelychallenging tasks for both radiologists and deep learning algorithms, but thepotential to learn from large and diverse datasets remains a promising avenueto increase their generalization capability both within- and across clinics. Toenable this for prototype-stage algorithms, where the majority of existingresearch remains, in this paper we introduce a flexible federated learningframework for cross-site training, validation, and evaluation of deep prostatecancer detection algorithms. Our approach utilizes an abstracted representationof the model architecture and data, which allows unpolished prototype deeplearning models to be trained without modification using the NVFlare federatedlearning framework. Our results show increases in prostate cancer detection andclassification accuracy using a specialized neural network model and diverseprostate biopsy data collected at two University of California researchhospitals, demonstrating the efficacy of our approach in adapting to differentdatasets and improving MR-biomarker discovery. We open-source our FLtoolssystem, which can be easily adapted to other deep learning projects for medicalimaging.

Federated Learning with Research Prototypes for Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology