SurgLaVi:用于手术视觉语言表示学习的大规模层次数据集
Alejandra Perez1, Chinedu Nwoye2, Ramtin Raji Kermani2
1Intuitive Surgical, Inc., Sunnyvale, CA, United States; Center for Research and Formation in Artificial Intelligence (CinfonIA), Universidad de los Andes, Bogotá, Colombia.
Medical image analysis
|February 20, 2026
概括
研究人员开发了SurgLaVi,这是一个大型的外科视觉语言数据集,以提高AI对手术视频的理解. 这一数据集可以更好地分析外科手术工作流程,并为增强的外科手术护理提供AI模型培训.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 视觉语言预训练 (VLP) 对外科视频分析具有前景,但受到小型,非多样化的数据集的限制.
- 现有的外科VLP数据集缺乏规模,程序多样性,语义深度和层次结构.
研究的目的:
- 介绍SurgLaVi,这是最大和最多样化的外科视觉语言数据集.
- 启用先进的AI模型,以理解和概括外科手术工作流程.
- 为更广泛的研究提供可访问的开源数据集 (SurgLaVi-β).
主要方法:
- 开发了一种完全自动化的管道,用于生成细粒度的外科视频转录和细分.
- 实现双模式过,以确保高质量,含义丰富的注释.
- 创建了SurgCLIP,一个CLIP风格的视频-文本对比框架,以评估数据集的实用性.
主要成果:
- SurgLaVi包含近2.4万个剪辑-标题对,跨越200多个程序,具有层次化的注释.
- SurgLaVi-β提供来自公共数据的113k片段-标题对,比之前的数据集大得多.
- 在手术阶段,步骤,动作和工具识别方面,SurgCLIP表现出了显著的改进.
结论:
- 大规模,含义丰富和层次结构化的数据集对于开发强大的外科AI至关重要.
- SurgLaVi是推进外科基础模型和人工智能驱动的外科应用的关键资源.
- 该数据集有助于在手术视频分析中更好地理解和概括.
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