Medical Image Analysis (MedIA) has become indispensable in modern healthcare, enhancing clinical diagnostics and personalized treatment. Despite the remarkable advancements supported by deep learning (DL) technologies, their practical deployment faces challenges posed by distribution shifts, where models trained on specific datasets underperform on others from varying hospitals, or patient populations. To address this issue, researchers have been actively developing strategies to increase the adaptability of DL models, enabling their effective use in unfamiliar environments. This paper systematically reviews approaches that apply DL techniques to MedIA systems affected by distribution shifts. Rather than organizing existing methods by technical characteristics, we explicitly bridge real-world clinical constraints -- such as limited data accessibility, strict privacy requirements, and heterogeneous collaboration protocols -- with the technical paradigms able to address them. By establishing this connection between operational constraints and methodological evolution, we categorize existing works into Joint Training, Federated Learning, Fine-tuning, and Domain Generalization, each aligned with specific healthcare scenarios. Beyond this taxonomy, our empirical analysis suggests that, as domain information becomes progressively less accessible across these paradigms, performance improvements become increasingly constrained, and further uncovers a gradual shift in methodological focus from explicit distribution alignment toward uncertainty-aware modeling, ultimately pointing to the need for more deployability-aware design in real-world MedIA.

翻译：医学图像分析（MedIA）已成为现代医疗中不可或缺的技术，极大地提升了临床诊断与个性化治疗水平。尽管深度学习技术取得了显著进展，但其实际部署仍面临分布偏移带来的挑战——即基于特定数据集训练的模型在来自不同医院或患者群体的数据上表现不佳。为解决这一问题，研究人员积极开发增强深度学习模型适应性的策略，使其能够在陌生环境中有效应用。本文系统回顾了将深度学习技术应用于受分布偏移影响的医学图像分析系统的方法。不同于按技术特征对现有方法进行分类，我们明确建立了现实临床约束（如有限的数据可及性、严格的隐私要求及异构协作协议）与能够应对这些约束的技术范式之间的桥梁。通过建立操作约束与方法论演进之间的关联，我们将现有研究归纳为联合训练、联邦学习、微调及领域泛化四类，每类对应特定的医疗场景。在此分类基础上，我们的实证分析表明，随着领域信息在这些范式中逐渐难以获取，性能提升愈发受限，并进一步揭示了方法论关注点从显式分布对齐向不确定性感知建模的渐进式转变，最终指向在实际医学图像分析中需要更注重可部署性的设计。