Due to the rapid advancements in recent years, medical image analysis is largely dominated
by deep learning (DL). However, building powerful and robust DL models requires training …

Federated learning (FL) enables collaboratively training a joint model for multiple medical
centers, while keeping the data decentralized due to privacy concerns. However, federated …

Federated learning is an emerging research paradigm for enabling collaboratively training
deep learning models without sharing patient data. However, the data from different …

Accepted: 12 January 2022 Deep Neural networks algorithms are recently used to solve
problems in medical imaging like no time ever. However, one of the main challenges for …

Federated learning (FL) enables collaborative learning of a deep learning model without
sharing the data of participating sites. FL in medical image analysis tasks is relatively new …

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 is a distributed paradigm that allows multiple parties to collaboratively
train deep models without exchanging the raw data. However, the data distribution among …

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 …

Limited training data and severe class imbalance impose significant challenges to
developing clinically robust deep learning models. Federated learning (FL) addresses the …

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 …