  Machine learning in medical imaging often faces a fundamental dilemma,
namely, the small sample size problem. Many recent studies suggest using
multi-domain data pooled from different acquisition sites/centers to improve
statistical power. However, medical images from different sites cannot be
easily shared to build large datasets for model training due to privacy
protection reasons. As a promising solution, federated learning, which enables
collaborative training of machine learning models based on data from different
sites without cross-site data sharing, has attracted considerable attention
recently. In this paper, we conduct a comprehensive survey of the recent
development of federated learning methods in medical image analysis. In this
survey, we first introduce the background knowledge of federated learning for
dealing with privacy protection and collaborative learning issues in medical
imaging. We then present a comprehensive review of recent advances in federated
learning methods for medical image analysis. Specifically, existing methods are
categorized based on three critical aspects of a federated learning system,
including client end, server end, and communication techniques. In each
category, we summarize the existing federated learning methods according to
specific research problems in medical image analysis and also provide insights
into the motivations of different approaches. In addition, we provide a review
of existing benchmark medical imaging datasets and software platforms for
current federated learning research. We also conduct an experimental study to
empirically evaluate typical federated learning methods for medical image
analysis. This survey can help to better understand the current research
status, challenges, and potential research opportunities in this promising
research field.
