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

no code implementations • 11 Jun 2022 • Abhejit Rajagopal, Ekaterina Redekop, Anil Kemisetti, Rushi Kulkarni, Steven Raman, Kirti Magudia, Corey W. Arnold, Peder E. Z. Larson

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.

Federated Learning

Mixed Supervision of Histopathology Improves Prostate Cancer Classification from MRI

no code implementations • 13 Dec 2022 • Abhejit Rajagopal, Antonio C. Westphalen, Nathan Velarde, Tim Ullrich, Jeffry P. Simko, Hao Nguyen, Thomas A. Hope, Peder E. Z. Larson, Kirti Magudia

To address this, we present an MRI-based deep learning method for predicting clinically significant prostate cancer applicable to a patient population with subsequent ground truth biopsy results ranging from benign pathology to ISUP grade group~5.