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 …

Federated learning enables multiple institutions to collaboratively train machine learning
models on their local data in a privacy-preserving way. However, its distributed nature often …

The collection and curation of large-scale medical datasets from multiple institutions is
essential for training accurate deep learning models, but privacy concerns often hinder data …

Federated learning is a machine learning method that allows decentralized training of deep
neural networks among multiple clients while preserving the privacy of each client's data …

Federated Learning (FL) obtained a lot of attention to the academic and industrial
stakeholders from the beginning of its invention. The eye-catching feature of FL is handling …

With recent developments in medical imaging facilities, extensive medical imaging data are
produced every day. This increasing amount of data provides an opportunity for researchers …

Hospitals and research institutions may not be willing to share their collected medical data
due to privacy concerns, transmission cost, and the intrinsic value of the data. Federated …

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

The performance of deep networks for medical image analysis is often constrained by
limited medical data, which is privacy-sensitive. Federated learning (FL) alleviates the …

Federated learning (FL) is a new kind of Artificial Intelligence (AI) aimed at data privacy
preservation that builds on decentralizing the training data for the deep learning model. This …