离临床部署还有多远？医学影像中完整无监督域自适应流程的评估
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging
- Ulm University Medical Center（乌尔姆大学医学中心）
- Ulm University（乌尔姆大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究评估医学影像中完整无监督域自适应流程，发现无目标标签时难选优模型，探索的两种策略可缩小差距，为推进UDA临床应用提供方向。
AI中文摘要：
将无监督域自适应（UDA）应用于临床实践需要选择使用哪种算法以及部署其哪个训练好的模型。然而，部署（目标）域是无标签的，因此无法在其上直接评估模型，导致难以选择合适的模型。我们通过同时考虑自适应和无标签选择，评估完整的UDA流程来解决该问题。本研究涵盖来自9个医学影像数据集的11个临床相关跨域场景，涉及10种UDA算法和13种无标签选择方法（验证器），总共评估超过80000个训练好的模型。通过研究发现，通常存在性能良好的自适应模型，但在无目标标签的情况下难以识别：验证器所选模型与最佳可用模型之间存在较大且结构性的目标性能差距，且所有评估的验证器均无一致的可靠性。为缩小该差距，我们探索了集成和少量目标标签预算两种策略，两者均能缩小差距但无法完全消除。总体而言，可部署的UDA依赖于完整流程；解决较少被探索的选择步骤可使当前UDA更接近临床应用。
英文摘要：
Deploying unsupervised domain adaptation (UDA) in clinical practice requires choosing which algorithm to use and which of its trained models to ship. However, the deployment (target) domain is unlabeled, so models cannot be evaluated directly on it, leaving it unclear which to select. We address this by evaluating the complete UDA pipeline, considering both adaptation and label-free selection together. Our study covers eleven clinically relevant cross-domain scenarios from nine medical imaging datasets, with ten UDA algorithms and 13 label-free selection methods (validators), evaluating over 80,000 trained models in total. By this, we find that a capable adapted model usually exists, but identifying it without target labels is difficult: the validator-selected models leave a large and structural target performance gap to the best available one, with no evaluated validator consistently reliable. Towards closing it, we explore two strategies, ensembling and a small target-labeling budget; both narrow this gap but do not close it entirely. Overall, deployable UDA depends on the complete pipeline; addressing the less explored selection step could bring much of current UDA closer to clinical use.
