# Video-based Surgical Skill Assessment using Tree-based Gaussian Process Classifier

arXiv: https://arxiv.org/abs/2312.10208 · HTML: https://mlsanity.com/paper/2312.10208/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 13 of 13)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [Video-based surgical skill assessment using 3D convolutional neural networks](https://mlsanity.com/paper/1903.02306/) (2019)
- [Representation Flow for Action Recognition](https://mlsanity.com/paper/1810.01455/) (2018)
- [Manipulation-skill Assessment from Videos with Spatial Attention Network](https://mlsanity.com/paper/1901.02579/) (2019)
- [Video and Accelerometer-Based Motion Analysis for Automated Surgical Skills Assessment](https://mlsanity.com/paper/1702.07772/) (2017)
- [Dependent Multinomial Models Made Easy: Stick Breaking with the P'olya-Gamma Augmentation](https://mlsanity.com/paper/1506.05843/) (2015)
- [GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning](https://mlsanity.com/paper/2102.07868/) (2021)
- [Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation](https://mlsanity.com/paper/1802.06383/) (2018)
- [Scalable Multi-Class Gaussian Process Classification using Expectation Propagation](https://mlsanity.com/paper/1706.07258/) (2017)
- [Accounting for Input Noise in Gaussian Process Parameter Retrieval](https://mlsanity.com/paper/2005.09907/) (2020)
- [Fast unsupervised Bayesian image segmentation with adaptive spatial regularisation](https://mlsanity.com/paper/1502.01400/) (2015)
- [Real-time Informative Surgical Skill Assessment with Gaussian Process Learning](https://mlsanity.com/paper/2112.02598/) (2021)

## Abstract (arXiv)

This paper aims to present a novel pipeline for automated surgical skill assessment using video data and to showcase the effectiveness of the proposed approach in evaluating surgeon proficiency, its potential for targeted training interventions, and quality assurance in surgical departments. The pipeline incorporates a representation flow convolutional neural network and a novel tree-based Gaussian process classifier, which is robust to noise, while being computationally efficient. Additionally, new kernels are introduced to enhance accuracy. The performance of the pipeline is evaluated using the JIGSAWS dataset. Comparative analysis with existing literature reveals significant improvement in accuracy and betterment in computation cost. The proposed pipeline contributes to computational efficiency and accuracy improvement in surgical skill assessment using video data. Results of our study based on comments of our colleague surgeons show that the proposed method has the potential to facilitate skill improvement among surgery fellows and enhance patient safety through targeted training interventions and quality assurance in surgical departments.