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

Dimitrios Anastasiou, Razvan Caramalau, Jialang Xu, Runlong He, Freweini Tesfai, Matthew Boal, Nader Francis, Danail Stoyanov, Evangelos B. Mazomenos

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.

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

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.

Paper Structure

This paper contains 7 sections, 1 equation, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Preliminaries
  4. Framework (CoRe-DA)
  5. Experiments
  6. Results
  7. Conclusions

Figures (4)

  • Figure 1: Sample sequences illustrating substantial domain shifts for surgical skill assessment (SSA), with target predictions from ViSA (SOTA) and CoRe-DA (ours).
  • Figure 2: Overview of the proposed CoRe-DA framework. Training uses source–exemplar–target triplets processed by a shared encoder and absolute and relative regression heads. Domain adaptation is driven by contrastive regression between source–exemplar and target–exemplar pairs and pseudo-label self-training.
  • Figure 3: Target-domain testing: Target videos are mixed with exemplar backgrounds, and predictions are reconstructed from relative scores computed across multiple pairs.
  • Figure 4: Scatter plots of predicted versus true scores. Paired t-tests show that CoRe-DA outperforms all competing methods with significant or marginally significant improvements (see $p$-values), further supported by higher $R^2$ and lower RMSE.