# ReCAP: Recursive Cross Attention Network for Pseudo-Label Generation in Robotic Surgical Skill Assessment

arXiv: https://arxiv.org/abs/2407.05180 · HTML: https://mlsanity.com/paper/2407.05180/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 22 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies](https://mlsanity.com/paper/2007.15911/) (2020)
- [LoViT: Long Video Transformer for Surgical Phase Recognition](https://mlsanity.com/paper/2305.08989/) (2023)
- [Towards Unified Surgical Skill Assessment](https://mlsanity.com/paper/2106.01035/) (2021)
- [Automated Surgical Skill Assessment in RMIS Training](https://mlsanity.com/paper/1712.08604/) (2017)
- [Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks](https://mlsanity.com/paper/1908.07319/) (2019)
- [Surgical Skill Assessment via Video Semantic Aggregation](https://mlsanity.com/paper/2208.02611/) (2022)
- [Deep Neural Networks for the Assessment of Surgical Skills: A Systematic Review](https://mlsanity.com/paper/2103.05113/) (2021)
- [Video-based Formative and Summative Assessment of Surgical Tasks using Deep Learning](https://mlsanity.com/paper/2203.09589/) (2022)
- [Surgical Skill Assessment on In-Vivo Clinical Data via the Clearness of Operating Field](https://mlsanity.com/paper/2008.11954/) (2020)
- [DaFoEs: Mixing Datasets towards the generalization of vision-state deep-learning Force Estimation in Minimally Invasive Robotic Surgery](https://mlsanity.com/paper/2401.09239/) (2024)

## Built on by (most cited first, 1 of 1)

- [SurgFusion-Net: Diversified Adaptive Multimodal Fusion Network for Surgical Skill Assessment](https://mlsanity.com/paper/2603.00108/) (2026)

## Abstract (arXiv)

In surgical skill assessment, the Objective Structured Assessments of Technical Skills (OSATS) and Global Rating Scale (GRS) are well-established tools for evaluating surgeons during training. These metrics, along with performance feedback, help surgeons improve and reach practice standards. Recent research on the open-source JIGSAWS dataset, which includes both GRS and OSATS labels, has focused on regressing GRS scores from kinematic data, video, or their combination. However, we argue that regressing GRS alone is limiting, as it aggregates OSATS scores and overlooks clinically meaningful variations during a surgical trial. To address this, we developed a weakly-supervised recurrent transformer model that tracks a surgeon's performance throughout a session by mapping hidden states to six OSATS, derived from kinematic data. These OSATS scores are averaged to predict GRS, allowing us to compare our model's performance against state-of-the-art (SOTA) methods. We report Spearman's Correlation Coefficients (SCC) demonstrating that our model outperforms SOTA using kinematic data (SCC 0.83-0.88), and matches performance with video-based models. Our model also surpasses SOTA in most tasks for average OSATS predictions (SCC 0.46-0.70) and specific OSATS (SCC 0.56-0.95). The generation of pseudo-labels at the segment level translates quantitative predictions into qualitative feedback, vital for automated surgical skill assessment pipelines. A senior surgeon validated our model's outputs, agreeing with 77% of the weakly-supervised predictions (p=0.006).