Summary of Gp-vls: a General-purpose Vision Language Model For Surgery, by Samuel Schmidgall et al.
GP-VLS: A general-purpose vision language model for surgery
by Samuel Schmidgall, Joseph Cho, Cyril Zakka, William Hiesinger
First submitted to arxiv on: 27 Jul 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 GP-VLS, a general-purpose vision language model for surgery, is introduced in this paper. The model integrates medical and surgical knowledge with visual scene understanding to enable natural language interaction. To evaluate the performance of GP-VLS, SurgiQual, a comprehensive benchmark, is proposed. This includes evaluating across medical and surgical knowledge benchmarks as well as surgical vision-language questions. Six new datasets are developed to train GP-VLS, covering medical knowledge, surgical textbooks, and vision-language pairs for tasks like phase recognition and tool identification. The results show that GP-VLS outperforms existing open- and closed-source models on surgical vision-language tasks by 8-21% across SurgiQual benchmarks. Additionally, GP-VLS demonstrates strong performance on medical and surgical knowledge tests compared to open-source alternatives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Surgery requires a combination of medical knowledge, visual assessment skills, and procedural expertise. This paper introduces a new AI model that can understand surgical scenes and interact through natural language. The model is trained using six new datasets and tested against existing models. It shows significant improvements in accuracy on tasks like recognizing phases and identifying tools. This model has the potential to support surgeons across a wide range of tasks and scenarios. |
Keywords
* Artificial intelligence * Language model * Scene understanding
