Medical Image Analysis (MedIA) has become an essential tool in medicine and
healthcare, aiding in disease diagnosis, prognosis, and treatment planning, and
recent successes in deep learning (DL) have made significant contributions to
its advances. However, DL models for MedIA remain challenging to deploy in
real-world situations, failing for generalization under the distributional gap
between training and testing samples, known as a distribution shift problem.
Researchers have dedicated their efforts to developing various DL methods to
adapt and perform robustly on unknown and out-of-distribution data
distributions. This paper comprehensively reviews domain generalization studies
specifically tailored for MedIA. We provide a holistic view of how domain
generalization techniques interact within the broader MedIA system, going
beyond methodologies to consider the operational implications on the entire
MedIA workflow. Specifically, we categorize domain generalization methods into
data-level, feature-level, model-level, and analysis-level methods. We show how
those methods can be used in various stages of the MedIA workflow with DL
equipped from data acquisition to model prediction and analysis. Furthermore,
we include benchmark datasets and applications used to evaluate these
approaches and analyze the strengths and weaknesses of various methods,
unveiling future research opportunities.

医学图像分析（MedIA）是医学和医疗中不可或缺的工具，对疾病诊断、预测和治疗计划有所助益，深度学习（DL）取得的重大成就对其发展作出了显著贡献。然而，用于 MedIA 的 DL 模型在实际情况中仍然具有挑战性，在训练和测试样本之间的分布差异下无法进行泛化，也即所谓的分布偏移问题。本文综述了专门针对 MedIA 的领域泛化研究，全面地回顾了领域泛化技术在更广泛的 MedIA 系统中的相互作用，超越方法论，考虑到对整个 MedIA 工作流程的操作影响。具体而言，我们将领域泛化方法分为数据级、特征级、模型级和分析级方法。我们展示了这些方法如何在 MedIA 工作流程的不同阶段中使用，从数据获取到模型预测和分析。此外，我们还包括了用于评估这些方法的基准数据集和应用，并分析了各种方法的优缺点，揭示了未来的研究机会。