Federated learning for medical image analysis: A survey.
Federated learning for medical image analysis: A survey.
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医学图像分析的联合学习：一项调查。
DOI：
10.1016/j.patcog.2024.110424
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发表时间：
2024
影响因子：
8
通讯作者：
Liu,Mingxia
中科院分区：
文献类型：
--
作者：
Guan,Hao;Yap,Pew-Thian;Bozoki,Andrea;Liu,Mingxia
医学成像中的机器学习经常面临一个基本的困境，即小样本问题。最近的许多研究建议使用从不同采集地点/中心汇集的多域数据来提高统计功效。然而，由于隐私保护的原因，来自不同站点的医学图像不能容易地共享以构建用于模型训练的大型数据集。作为一种有前途的解决方案，联邦学习，它使机器学习模型的协作训练基于来自不同站点的数据，而无需跨站点数据共享，最近引起了相当大的关注。在本文中，我们对联邦学习方法在医学图像分析中的最新发展进行了全面的调查。我们系统地收集了2017年至2023年期间发表的关于联邦学习及其在医学图像分析中的应用的研究论文。我们使用IEEE Xplore、ACM Digital Library、Science Direct、Springer Link、Web of Science、Google Scholar和PubMed的数据库进行检索和编辑。在本调查中，我们首先介绍了联邦学习的背景知识，用于处理医学成像中的隐私保护和协作学习问题。然后，我们对用于医学图像分析的联邦学习方法的最新进展进行了全面的综述。具体而言，现有的方法进行分类的基础上的三个关键方面的联邦学习系统，包括客户端，服务器端，和通信技术。在每个类别中，我们根据医学图像分析中的具体研究问题总结了现有的联邦学习方法，并提供了不同方法的动机。此外，我们还对当前联邦学习研究的现有基准医学成像数据集和软件平台进行了回顾。我们还进行了一项实验研究，以经验评估典型的联邦学习方法用于医学图像分析。本次调查有助于更好地了解这一有前途的研究领域的研究现状、挑战和潜在的研究机会。
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. We have systematically gathered research papers on federated learning and its applications in medical image analysis published between 2017 and 2023. Our search and compilation were conducted using databases from IEEE Xplore, ACM Digital Library, Science Direct, Springer Link, Web of Science, Google Scholar, and PubMed. 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.