Federated learning (FL) has emerged with increasing popularity to collaborate distributed
medical institutions for training deep networks. However, despite existing FL algorithms only …

Federated learning (FL) refers to a system in which a central aggregator coordinates the
efforts of several clients to solve the issues of machine learning. This setting allows the …

Clinically oriented deep learning algorithms, combined with large-scale medical datasets,
have significantly promoted computer-aided diagnosis. To address increasing ethical and …

Federated learning (FL) is a distributed machine learning technique that enables
collaborative model training while avoiding explicit data sharing. The inherent privacy …

Deep learning models can enable accurate and efficient disease diagnosis, but have thus
far been hampered by the data scarcity present in the medical world. Automated diagnosis …

The global pandemic of coronavirus disease 2019 (COVID-19) is continuing to have a
significant effect on the well-being of the global population, thus increasing the demand for …

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 …

With the advent of deep learning and increasing use of brain MRIs, a great amount of
interest has arisen in automated anomaly segmentation to improve clinical workflows; …

The purpose of federated learning is to enable multiple clients to jointly train a machine
learning model without sharing data. However, the existing methods for training an image …

Federated learning (FL) is a promising way to use the computing power of mobile devices
while maintaining the privacy of users. Current work in FL, however, makes the unrealistic …