  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
