Summary of General Surgery Vision Transformer: a Video Pre-trained Foundation Model For General Surgery, by Samuel Schmidgall et al.
General surgery vision transformer: A video pre-trained foundation model for general surgery
by Samuel Schmidgall, Ji Woong Kim, Jeffrey Jopling, Axel Krieger
First submitted to arxiv on: 9 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Tissues and Organs (q-bio.TO)
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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 paper addresses a significant obstacle in computational research for surgery by releasing a massive dataset of general surgery videos and a foundation model designed specifically for this field. The dataset consists of 680 hours of surgical videos featuring various procedures and techniques, including robotic and laparoscopic methods. A novel approach is proposed to pre-train a General Surgery Vision Transformer (GSViT) using forward video prediction, which can be used in real-time applications. Additionally, the authors provide code and weights for procedure-specific fine-tuned versions of GSViT for 10 procedures. The performance of GSViT is demonstrated on the Cholec80 phase annotation task, outperforming state-of-the-art single frame predictors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps make surgery research easier by sharing a huge collection of surgery videos and a special computer model designed just for surgery. This model can be used in real-time during surgeries to help with things like identifying what’s happening on the screen. The researchers also share code and models that are specific to different types of surgical procedures. They show how well this model works by testing it on a task where it has to identify phases of a cholecystectomy procedure. |
Keywords
* Artificial intelligence * Vision transformer
