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, deploying DL models for MedIA in real-world situations remains challenging due to their failure to generalize across the distributional gap between training and testing samples - a problem known as domain shift. 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 critically analyze the strengths and weaknesses of various methods, unveiling future research opportunities.

翻译：医学图像分析已成为医学和医疗保健领域的重要工具，有助于疾病诊断、预后和治疗规划，而深度学习近期的成功为其进步做出了重大贡献。然而，在现实世界中部署用于医学图像分析的深度学习模型仍然具有挑战性，原因在于它们无法泛化训练样本与测试样本之间的分布差异——这一问题被称为域偏移。研究人员致力于开发各种深度学习方法，使其能够适应并稳健地处理未知和分布外的数据分布。本文全面综述了专门针对医学图像分析的域泛化研究。我们从整体视角审视域泛化技术如何在更广泛的医学图像分析系统中相互作用，超越方法论层面，考虑其对整个医学图像分析工作流程的操作影响。具体而言，我们将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应用于配备深度学习的医学图像分析工作流程的各个阶段，从数据采集到模型预测与分析。此外，我们批判性地分析了各种方法的优势与不足，并揭示了未来的研究机会。