Federated Learning-Driven Collaborative Diagnostic System for Metastatic Breast Cancer

Metastatic breast cancer is one of the leading causes of cancer mortality. While there has been progress in developing deep learning-driven diagnostic system for metastatic breast cancer based on histopathological images, it faces a major challenge in real-world application, i.e., how to improve generalizability of the diagnostic models for diverse patient populations and variations in sample and image processing across different geographic regions. Multi-institutional collaborative learning, which trains a single model using data from multiple institutions, can address the challenges of data inadequacy, lack of data diversity, and data quality. However, the current practice of direct medical data transfer among institutions faces increasing restrictions including patient privacy, intellectual property, data ownership and legal obligations. To enable multi-institutional collaborative learning for cancer diagnosis, we developed a federated learning-driven collaborative diagnostic system for metastatic breast cancer. This system preserves patient privacy by decoupling model training from the need for direct medical data transfer between institutions. Further, this study has demonstrated that the federate learning-driven system can improve diagnostic model accuracy and generalizability by leveraging information derived from diverse data sources across multiple institutions. This study has also shown that this collaborative diagnostic system has a strong potential of improving local diagnosis performance on lower quality images at a resource constrained institution. Our research indicated that federated learning-driven diagnostic system is a feasible and robust framework for multi-institutional collaborative development of diagnostic models for metastatic breast cancer. In addition to the benefits of improving diagnostic model generalizability for diverse patient populations, the collaborative diagnostic system presents a new opportunity to enable under-resourced healthcare institutions to leverage external data resources to improve local diagnosis performance while preserving patient privacy.