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：医学成像中的机器学习常面临一个根本性困境，即小样本量问题。近期许多研究表明，利用来自不同采集站点/数据集的多域数据可提升统计效能。然而，由于隐私保护原因，不同站点的医学图像难以共享以构建大规模训练数据集。联邦学习作为一种有前景的解决方案，能够在无需跨站点数据共享的情况下，基于不同站点的数据实现机器学习模型的协同训练，近期已引起广泛关注。本文对医学图像分析中联邦学习方法的最新进展进行了全面综述。首先介绍联邦学习应对医学成像中隐私保护与协同学习问题的背景与动机，继而系统梳理当前联邦学习方法在医学图像分析领域的最新成果。具体而言，现有方法依据联邦学习系统的三个关键维度（客户端、服务器端与通信技术）进行分类。在每个类别中，我们针对医学图像分析的具体研究问题总结现有联邦学习方案，并深入剖析不同方法的动机机理。此外，本文还综述了当前联邦学习研究中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。本综述有助于更深入理解这一新兴研究领域的发展现状、挑战与潜在研究机遇。