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/datasets 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. We first introduce the background and motivation 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.

翻译：医学图像中的机器学习常面临一个根本性难题，即小样本问题。近期许多研究提出利用来自不同采集站点/数据集的多域数据来提升统计效能，但受隐私保护限制，不同站点的医学图像难以共享以构建大规模训练数据集。作为有前景的解决方案，联邦学习能够在不进行跨站点数据共享的前提下，实现基于多站点数据的机器学习模型协同训练，近年来备受关注。本文对联邦学习方法在医学图像分析领域的最新进展进行了全面综述。首先介绍联邦学习在医学影像中处理隐私保护与协同学习问题的背景与动机，随后系统梳理近期联邦学习方法在医学图像分析中的突破性成果。具体而言，现有方法基于联邦学习系统的三个关键要素（客户端端、服务端端与通信技术）进行分类。在每个类别中，我们根据医学图像分析中的特定研究问题归纳现有联邦学习方法，并剖析不同方法的动因。此外，本文还综述了当前联邦学习研究常用的基准医学影像数据集与软件平台，并通过实验研究对典型联邦学习方法进行实证评估。本综述有助于深入理解该前沿研究领域的发展现状、挑战与潜在研究机遇。