ShangHai University, China

Talk Title: Federated learning for multicenter collaborative disease diagnosis

Abstract:
Current computer-aided diagnosis system with deep learning method plays an important role in the field of medical imaging. The collaborative diagnosis of diseases by multiple medical institutions has become a popular trend. However, large scale annotations put heavy burdens on medical experts.Furthermore, the centralized learning system has defects in privacy protection and model generalization. To meet these challenges, we propose two federated learning methods for multicenter collaborative diagnosis of diseases: Federated active learning for multicenter collaborative disease diagnosis (FAL) and Federated ensemble learning for non-iid data (FedEL). The proposed FAL method outperforms state-of-the-art methods on segmentation and classification tasks for multicenter collaborative disease diagnosis. The proposed FedEL method demonstrates good generalization ability in experiments across different datasets, including natural scene image datasets and medical image datasets.