CoRe-DA：基于对比学习的无监督领域适应在手术技能评估中的应用
CoRe-DA: Contrastive Regression for Unsupervised Domain Adaptation in Surgical Skill Assessment
- UCL Hawkes Institute, University College London（伦敦大学学院霍克斯研究所）
- Dept of Computer Science, University College London（伦敦大学学院计算机科学系）
- The Griffin Institute（格里芬研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出CoRe-DA，一种基于对比学习的无监督领域适应方法，用于手术技能评估，通过相对评分监督和目标域自训练，提升跨领域泛化能力，优于现有方法。
AI中文摘要：
基于视觉的手术技能评估（SSA）能够客观评估手术性能。该领域的发展受到手动标注定量技能评分高成本和时间需求的限制，以及现有回归模型在新手术任务和环境中的泛化能力差。同时，大量未标注视频数据的可用性促使开发无监督领域适应（UDA）方法用于SSA。我们介绍了首个SSA回归UDA基准，涵盖四个数据集，包括干实验室和临床环境以及开放和机器人手术。我们评估了八种代表性模型，在具有挑战性的领域转移下提出CoRe-DA，一种新的基于对比学习的适应框架。我们的方法通过相对评分监督和目标域自训练学习领域不变表示。在两个UDA设置中的全面实验表明，CoRe-DA优于现有方法，分别在干实验室和临床目标数据集上达到Spearman相关系数0.46和0.41，无需使用任何标记的目标数据进行训练。总体而言，CoRe-DA实现了可扩展的SSA，具有可靠的跨领域泛化能力，其中现有方法表现不佳。我们的代码和数据集将在https://github.com/anastadimi/CoRe-DA上发布。
英文摘要：
Vision-based surgical skill assessment (SSA) enables objective and scalable evaluation of operative performance. Progress in this field is constrained by the high cost and time demands for manual annotation of quantitative skill scores, as well as the poor generalization of existing regression models to new surgical tasks and environments. Meanwhile, appreciable volumes of unlabeled video data are now available, motivating the development of unsupervised domain adaptation (UDA) methods for SSA. We introduce the first benchmark for UDA in SSA regression, spanning four datasets across dry-lab and clinical settings as well as open and robotic surgery. We evaluate eight representative models under challenging domain shifts and propose CoRe-DA, a novel contrastive regression-based adaptation framework. Our method learns domain-invariant representations through relative-score supervision and target-domain self-training. Comprehensive experiments across two UDA settings show that CoRe-DA is superior to state-of-the-art methods, achieving Spearman Correlation Coefficients of 0.46 and 0.41 on dry-lab and clinical target datasets, respectively, without using any labeled target data for training. Overall, CoRe-DA enables scalable SSA with reliable cross-domain generalization, where existing methods underperform. Our code and datasets will be released at https://github.com/anastadimi/CoRe-DA.
