Abstract
Medical imaging models frequently fail when deployed across hospitals, scanners, populations, or imaging protocols due to domain shift, limiting their clinical reliability. While transfer learning and domain adaptation address such shifts statistically, they often rely on spurious correlations that break under changing conditions. On the other hand, causal inference provides a principled way to identify invariant mechanisms that remain stable across environments. This survey introduces and systematises Causal Transfer Learning (CTL) for medical image analysis. This paradigm integrates causal reasoning with cross-domain representation learning to enable robust and generalisable clinical AI. We frame domain shift as a causal problem and analyse how structural causal models, invariant risk minimisation, and counterfactual reasoning can be embedded within transfer learning pipelines. We studied spanning classification, segmentation, reconstruction, anomaly detection, and multimodal imaging, and organised them by task, shift type, and causal assumption. A unified taxonomy is proposed that connects causal frameworks and transfer mechanisms. We further summarise datasets, benchmarks, and empirical gains, highlighting when and why causal transfer outperforms correlation-based domain adaptation. Finally, we discuss how CTL supports fairness, robustness, and trustworthy deployment in multi-institutional and federated settings, and outline open challenges and research directions for clinically reliable medical imaging AI.
Abstract (translated)
医学影像模型在跨医院、扫描仪、人群或成像协议部署时，常因域偏移而失效，限制了其临床可靠性。尽管迁移学习和域适应从统计角度缓解此类偏移，但它们往往依赖在条件变化时易失效的虚假关联。另一方面，因果推理提供了一种识别跨环境保持稳定的不变机制的规范方法。本综述针对医学影像分析提出并系统化了因果迁移学习（CTL）范式。该范式将因果推理与跨域表示学习相结合，以实现鲁棒且可泛化的临床人工智能。我们将域偏移构建为因果问题，并分析了如何将结构因果模型、不变风险最小化和反事实推理嵌入迁移学习流程。我们研究了分类、分割、重建、异常检测和多模态成像等任务，按任务类型、偏移类别和因果假设进行组织。提出了统一分类法，连接因果框架与迁移机制。进一步总结了数据集、基准和实证增益，强调了因果迁移在何时及为何优于基于关联的域适应。最后讨论了CTL如何支持多机构及联邦学习场景下的公平性、鲁棒性和可信部署，并概述了实现临床可靠医学影像人工智能的开放挑战与研究方向。
URL
https://arxiv.org/abs/2603.24388
