EndoChat:用于内镜外科手术的接地式多模式大语言模型
Guankun Wang1, Long Bai2, Junyi Wang3
1The Chinese University of Hong Kong, 999077, Hong Kong Special Administrative Region of China; Theory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co. Ltd., 999077, Hong Kong Special Administrative Region of China.
Medical image analysis
|September 10, 2025
概括
新的多式大型语言模型 (MLLM) EndoChat 增强了机器人辅助手术培训和决策. 它在内镜手术的理解方面表现出色,提供先进的手术场景分析和对话能力.
科学领域:
- 人工智能的人工智能
- 医疗机器人 医疗机器人
- 计算机视觉 计算机视觉
背景情况:
- 多模式大语言模型 (MLLMs) 在计算机辅助诊断和机器人辅助手术方面表现有前途.
- 在专门的MLLM中存在一个关于内镜外科手术场景理解的差距.
研究的目的:
- 介绍EndoChat,一个旨在理解内镜手术的MLLM.
- 开发一个专门的数据集和新的机制,以改善手术场景分析.
主要方法:
- 使用新型的外科信息提取和注释管道构建了Surg-396K数据集.
- 实现了一个多尺度的视觉令牌交互机制.
- 介绍了一种基于视觉对比的推理机制,用于增强学习.
主要成果:
- 在五个对话范式和七个手术场景理解任务上取得了最先进的表现.
- 外科医生对EndoChat的对话案例生成提供了积极的反.
- 证明了在机器人辅助外科手术中推进培训和自动化的潜力.
结论:
- 在手术应用中,EndoChat代表了MLLM的重大进步.
- 开发的数据集和模型可以加速机器人辅助手术培训和自动化方面的进展.
- 数据集和模型的公开可用性有助于进一步的研究和开发.
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