CoRe-DA: Contrastive Regression for Unsupervised Domain Adaptation in Surgical Skill Assessment

2026-03-31

AI Summary

This work addresses the high annotation cost in surgical skill assessment and the limited cross-task and cross-environment generalization of existing regression models by proposing an unsupervised domain adaptation framework based on contrastive regression. It establishes, for the first time, an unsupervised domain adaptive regression benchmark for surgical skill evaluation, leveraging relative score-based contrastive learning combined with self-training on the target domain to learn domain-invariant representations—enabling cross-domain assessment without requiring target-domain labels. Evaluated on both dry-lab and clinical datasets, the method achieves Spearman correlation coefficients of 0.46 and 0.41, respectively, significantly outperforming current state-of-the-art approaches.

Abstract

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.

Problem

• surgical skill assessment
• unsupervised domain adaptation
• cross-domain generalization
• vision-based evaluation
• regression

Innovation

• Contrastive Regression
• Unsupervised Domain Adaptation
• Surgical Skill Assessment
• Domain-Invariant Representation
• Relative-Score Supervision