Medical Image Analysis (MedIA) has emerged as a crucial tool in computer-aided diagnosis systems, particularly with the advancement of deep learning (DL) in recent years. However, well-trained deep models often experience significant performance degradation when deployed in different medical sites, modalities, and sequences, known as a domain shift issue. In light of this, Domain Generalization (DG) for MedIA aims to address the domain shift challenge by generalizing effectively and performing robustly across unknown data distributions. This paper presents the a comprehensive review of substantial developments in this area. First, we provide a formal definition of domain shift and domain generalization in medical field, and discuss several related settings. Subsequently, we summarize the recent methods from three viewpoints: data manipulation level, feature representation level, and model training level, and present some algorithms in detail for each viewpoints. Furthermore, we introduce the commonly used datasets. Finally, we summarize existing literature and present some potential research topics for the future. For this survey, we also created a GitHub project by collecting the supporting resources, at the link: https://github.com/Ziwei-Niu/DG_for_MedIA

翻译：医学图像分析作为计算机辅助诊断系统的关键工具，近年来随着深度学习的发展取得了显著进展。然而，经过训练的深度模型在应用于不同医疗站点、模态和序列时，常出现性能显著下降的问题，即领域偏移现象。为此，面向医学图像分析的领域泛化旨在通过有效泛化和对未知数据分布的鲁棒性来解决领域偏移挑战。本文对该领域的重要进展进行了全面综述。首先，我们给出了医学领域中领域偏移和领域泛化的正式定义，并讨论了相关设定。随后，从数据操作层面、特征表示层面和模型训练层面三个视角总结了近期方法，并针对每个视角详细介绍了部分算法。此外，我们介绍了常用数据集。最后，我们总结了现有文献，并提出了未来潜在的研究方向。为配合本综述，我们还创建了GitHub项目收集相关资源，链接为：https://github.com/Ziwei-Niu/DG_for_MedIA