  Early prostate cancer detection and staging from MRI are extremely
challenging tasks for both radiologists and deep learning algorithms, but the
potential to learn from large and diverse datasets remains a promising avenue
to increase their generalization capability both within- and across clinics. To
enable this for prototype-stage algorithms, where the majority of existing
research remains, in this paper we introduce a flexible federated learning
framework for cross-site training, validation, and evaluation of deep prostate
cancer detection algorithms. Our approach utilizes an abstracted representation
of the model architecture and data, which allows unpolished prototype deep
learning models to be trained without modification using the NVFlare federated
learning framework. Our results show increases in prostate cancer detection and
classification accuracy using a specialized neural network model and diverse
prostate biopsy data collected at two University of California research
hospitals, demonstrating the efficacy of our approach in adapting to different
datasets and improving MR-biomarker discovery. We open-source our FLtools
system, which can be easily adapted to other deep learning projects for medical
imaging.
