Summary of Multi-modal Self-supervised Learning For Surgical Feedback Effectiveness Assessment, by Arushi Gupta et al.
Multi-Modal Self-Supervised Learning for Surgical Feedback Effectiveness Assessment
by Arushi Gupta, Rafal Kocielnik, Jiayun Wang, Firdavs Nasriddinov, Cherine Yang, Elyssa Wong, Anima Anandkumar, Andrew Hung
First submitted to arxiv on: 17 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 Machine learning educators can use this paper’s method to develop an automated system that accurately predicts whether real-time verbal feedback from trainers leads to changes in trainee behavior during surgical training. The proposed approach integrates transcribed verbal feedback and corresponding surgical video to predict effectiveness, achieving an AUROC of 0.70+/-0.02. This paper introduces self-supervised fine-tuning as a strategy for enhancing surgical video representation learning, which improves prediction performance by up to 6.6%. The method has potential applications in developing methods for improving surgical training and education. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary During surgical training, real-time feedback from trainers is important. But it’s hard to figure out if this feedback makes trainees change their behavior. That’s why we need a way to automatically see if the feedback works. This paper shows how to do that using words and pictures. It uses special computer learning tricks to predict if the feedback will make trainees behave differently. The results are pretty good, with a score of 0.70+/-0.02. This could help us improve surgical training and education. |
Keywords
» Artificial intelligence » Fine tuning » Machine learning » Representation learning » Self supervised
