Federated Learning for Utilizing Multi-Institutional Prostate MRI with Diverse Histopathology

Prostate cancer screening and diagnosis from MRI is extremely challenging, and current machine learning algorithms suffer in cross-institutional generalizability. Federated learning is a way to alleviate these issues by combining multi-center data without aggregating or homogenizing data. To enable this for prototype-stage algorithms, we introduce FLtools, a lightweight python library with re-usable federated learning components available freely at https://federated.ucsf.edu. We use this federated learning system to train a 3D UCNet on bi-parametric MRI and paired prostate biopsy data from two University of California hospitals, demonstrating dramatic improvements in cross-site generalization accuracy in clinically-significant lesion classification.