Summary of Hecvl: Hierarchical Video-language Pretraining For Zero-shot Surgical Phase Recognition, by Kun Yuan et al.
HecVL: Hierarchical Video-Language Pretraining for Zero-shot Surgical Phase Recognition
by Kun Yuan, Vinkle Srivastav, Nassir Navab, Nicolas Padoy
First submitted to arxiv on: 16 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents a novel approach to developing generalist surgical models using natural language processing. The proposed method, HecVL, combines hierarchical video-text paired datasets with a fine-to-coarse contrastive learning framework to learn multi-modal representations that encode short-term and long-term surgical concepts. This approach enables zero-shot surgical phase recognition without human annotation and allows for transfer across different surgical procedures and medical centers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to create general-purpose models for surgery using natural language processing. They combined videos of surgeries with written descriptions at different levels, from detailed steps to overall procedure summaries. This helps the model learn about short-term actions and long-term concepts. The approach worked well in recognizing surgical phases without needing any human help, and it also transferred well across different procedures and hospitals. |
Keywords
» Artificial intelligence » Multi modal » Natural language processing » Zero shot
