Medical image analysis using deep neural networks (DNN) has demonstrated state-of-the-
art performance in image classification and segmentation tasks, aiding disease diagnosis …

Abstract Machine learning in medical imaging often faces a fundamental dilemma, namely,
the small sample size problem. Many recent studies suggest using multi-domain data …

Artificial intelligence (AI) has led to a high rate of development in healthcare, and good
progress has been made on many complex medical problems. However, there is a lack of …

Due to the fast advancement of artificial intelligence (AI), centralized-based models have
become critical for healthcare tasks like in medical image analysis and human behavior …

One of the key challenges in federated learning (FL) is local data distribution heterogeneity
across clients, which may cause inconsistent feature spaces across clients. To address this …

With the rapid development of the Internet of Medical Things (IoMT), medical institutions are
accumulating vast amounts of medical data and aiming to utilize this data to train high …

Federated learning (FL), training deep models from decentralized data without privacy
leakage, has shown great potential in medical image computing recently. However …

Existing federated learning works mainly focus on the fully supervised training setting. In
realistic scenarios, however, most clinical sites can only provide data without annotations …

Federated learning (FL) is gaining wide acceptance across the medical AI domains. FL
promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability …

In terms of increasing privacy issues, Federated Learning (FL) has received extensive
attention in medical imaging. Through collaborative training, FL can produce superior …