SurgflowNet:利用未注释的视频来实现一致的内镜垂体外科手术工作流程识别
Anjana Wijekoon1, Adrito Das2, Zhehua Mao1
1UCL Hawkes Institute, University College London, London, United Kingdom; Department of Computer Science, University College London, London, United Kingdom.
Artificial intelligence in medicine
|December 2, 2025
概括
通过使用未注释的数据,SurgflowNet增强了内镜垂体外科手术工作流程识别. 这种新的方法比目前的技术提高了准确性,有助于外科医生的培训和效率.
科学领域:
- 神经外科 神经外科
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 自动化手术工作流程识别对于培训和效率至关重要.
- 内镜垂体外科手术由于复杂,可变的工作流程而带来独特的挑战.
- 目前的方法在垂体外科手术中扎着数据稀缺和可转移性.
研究的目的:
- 开发一种新型模型,用于一致的内镜垂体外科手术工作流程识别.
- 利用未注释的数据来克服注释的限制.
- 提高复杂手术过程中外科工作流程识别的准确性和稳定性.
主要方法:
- 开发了SurgflowNet,这是一个用于工作流识别的时空模型.
- 利用有限的注释数据集推断未注释视频的准标签.
- 采用学生-教师框架用于编码器培训和稳定性的一致性损失.
主要成果:
- 与最先进的方法相比,SurgflowNet显示出了显著的改进.
- 在宏观F1得分中增加了5%,在编辑得分中增加了13.4%.
- 成功利用未注释的数据进行强大的模型训练.
结论:
- SurgflowNet为内镜垂体外科手术工作流程识别提供了一个强大的解决方案.
- 该模型使用无注释数据的能力解决了该领域的一个关键挑战.
- 这种进步有可能提高外科培训和手术内效率.
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