Case Study: Emory University advances breast cancer diagnosis with Rhino Federated Computing

Transforming Breast Cancer Diagnosis with Federated Computing A Cross-Atlantic Collaboration

Emory University faced the urgent global challenge of improving breast cancer diagnostics, which requires collaboration on sensitive patient data that is restricted by privacy regulations. To advance its research, the university partnered with Rhino Federated Computing and leveraged its Federated Computing Platform to enable secure, multi-institutional collaboration without transferring data.

Rhino's solution utilized a federated approach, allowing a deep learning algorithm developed at Emory to be validated on a unique dataset of 500 patient records from partners in Israel, all while the data remained securely behind the local firewalls. This privacy-preserving collaboration achieved a new benchmark in diagnostic accuracy with a mean average precision of 0.82, successfully demonstrating the algorithm's generalizability and significantly accelerating the pace of medical research across continents.

Emory University

German Corredor Prada

Assistant Professor