arXiv cs.AI· Yiheng Xiong, Luisa Gall\'ee, Daniel Santak Wolf, Heiko Hillenhagen, Michael G\"otz·· 6 小时前AI 评分42
距临床落地还有多远？医学影像中无监督域适应完整流程的评估
How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imaging
AI 导读
一项被 WACV 2027 接收的 arXiv 研究评估了医学影像中无监督域适应（UDA）的完整部署流程，涵盖 9 个数据集的 11 个跨域场景、10 种 UDA 算法和 13 种免标签模型选择方法，共评估超 80,000 个训练模型。
正文 · 原文
Abstract: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.
| Comments: | Accepted to WACV 2027 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.12035 [cs.CV] |
| (or arXiv:2608.12035v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2608.12035 arXiv-issued DOI via DataCite |
Submission history
From: Yiheng Xiong [view email]
[v1]
Wed, 12 Aug 2026 13:19:14 UTC (366 KB)
[v2]
Sun, 25 Oct 2026 14:50:25 UTC (374 KB)
来源：arXiv cs.AI · arxiv.org