Non-parametric regularization for class imbalance federated medical image classification
This addresses class imbalance challenges in federated medical image analysis, though it appears incremental as it builds on existing FL methods with a new regularization technique.
The paper tackles the problem of class imbalance in federated learning for medical image classification by proposing non-parametric regularization methods (FedNPR and FedNPR-Per), which outperform existing state-of-the-art approaches in skin lesion classification and intracranial hemorrhage identification.
Limited training data and severe class imbalance pose significant challenges to developing clinically robust deep learning models. Federated learning (FL) addresses the former by enabling different medical clients to collaboratively train a deep model without sharing privacy-sensitive data. However, class imbalance worsens due to variation in inter-client class distribution. We propose federated learning with non-parametric regularization (FedNPR and FedNPR-Per, a personalized version of FedNPR) to regularize the feature extractor and enhance useful and discriminative signal in the feature space. Our extensive experiments show that FedNPR outperform the existing state-of-the art FL approaches in class imbalance skin lesion classification and intracranial hemorrhage identification. Additionally, the non-parametric regularization module consistently improves the performance of existing state-of-the-art FL approaches. We believe that NPR is a valuable tool in FL under clinical settings.