发表机构
Ulm University Medical Center; Ulm University（乌尔姆大学医学中心; 乌尔姆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对医学影像无监督域适应算法选择的临床难题，提出无标注准则联合选择算法与超参数，在多数据集多场景实验中表现优于其他方法。
AI 中文摘要
现有大量无监督域适应（UDA）算法，但在临床实践中，因未标注的部署（目标）域无法直接评估，选择最合适的算法及恰当超参数往往仍不明确。我们提出一种无标注准则，可联合选择UDA的算法与超参数。给定由多种算法以不同超参数训练的候选模型池，我们的方法将每个候选模型与一致性参考进行评分，选择得分最高者。该一致性参考在不使用目标标签的情况下分两级构建：首先，利用多种无标注选择信号，每种信号在每种算法中提名一个模型；其次，将各算法提名的模型聚合，为每个未标注目标样本形成参考预测。随后选择预测与该参考一致性最高的候选模型用于部署。在四个脑部MRI和四个胸部X射线数据集、七个临床相关迁移场景上的实验结果表明，我们的方法比其他方法实现了更优的选择性能，且在不同算法池中仍保持有效，为UDA的临床部署提供了实用的无标注算法选择方向。
英文摘要
Numerous unsupervised domain adaptation (UDA) algorithms exist, but for clinical practice, selecting the best-suited one along with proper hyperparameters often remains unclear, as the unlabeled deployment (target) domain prevents direct evaluation. We propose a label-free criterion that jointly selects the algorithm and hyperparameters for UDA. Given a pool of candidate models from multiple algorithms trained with different hyperparameters, our approach scores each candidate against an agreement reference, and selects the one with the highest score. The agreement reference is constructed in two levels without using target labels. First, we leverage multiple label-free selection signals, using each to nominate a model within every algorithm. Second, the nominated models are aggregated across algorithms to form a reference prediction for each unlabeled target sample. The candidate whose predictions agree most with this reference is then selected for deployment. Experimental results on four brain MRI and four chest X-ray datasets across seven clinically relevant transfer scenarios show that our method achieves better selection performance than other methods and remains effective across different algorithm pools. Our approach takes a step towards practical, label-free algorithm selection for clinical deployment of UDA.
CommentsAccepted to AMAI @ MICCAI 2026
