Enhancing Performance for Highly Imbalanced Medical Data via Data
Regularization in a Federated Learning Setting
Enhancing Performance for Highly Imbalanced Medical Data via Data
Regularization in a Federated Learning Setting
The increased availability of medical data has significantly impacted healthcare by enabling the application of machine / deep learning approaches in various instances. However, medical datasets are usually small and scattered across multiple providers, suffer from high class-imbalance, and are subject to stringent data privacy constraints. In this paper, the …