Summary of Think Step by Step: Chain-of-gesture Prompting For Error Detection in Robotic Surgical Videos, By Zhimin Shao et al.
Think Step by Step: Chain-of-Gesture Prompting for Error Detection in Robotic Surgical Videos
by Zhimin Shao, Jialang Xu, Danail Stoyanov, Evangelos B. Mazomenos, Yueming Jin
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel Chain-of-Thought (COG) prompting framework presented in this paper is a real-time end-to-end error detection system for robot-assisted minimally invasive surgery (RMIS). It leverages contextual information from surgical videos to improve performance, unlike current methods that rely on accurate gesture identification. The COG framework consists of two reasoning modules: Gestural-Visual Reasoning and Multi-Scale Temporal Reasoning. These modules are designed to mimic the decision-making processes of expert surgeons, utilizing transformer and attention architectures for gesture prompting, as well as a multi-stage temporal convolutional network with both slow and fast paths for temporal information extraction. The framework is validated on the public benchmark RMIS dataset JIGSAWS, outperforming state-of-the-art methods by 4.6% in F1 score, 4.6% in Accuracy, and 5.9% in Jaccard index, while processing each frame in 6.69 milliseconds on average. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way to detect errors in robot-assisted surgery using video recordings. Current methods are not very good because they rely on correctly identifying specific movements the surgeon makes. This can be tricky because it’s hard to capture all the important details just by looking at the surgeon’s hands and instruments. The authors of this paper came up with a new approach that takes into account many more factors, like what the surgeon is doing and why. They tested their method on a big dataset of videos and found that it worked much better than existing methods. |
Keywords
» Artificial intelligence » Attention » Convolutional network » F1 score » Prompting » Transformer
