一般化的少数镜头MM-Former用于外科手术场景泛光细分
Xiaoyan Zhang1, Liming Wu2, Zhichen Wang2
1Key Laboratory for Biomedical Engineering of Ministry of Education College of Biomedical Engineering and Instrument Science Zhejiang University Zhejiang China.
Healthcare technology letters
|December 11, 2025
概括
这项研究引入了一种用于外科手术全光学细分的新型几次射击学习方法,克服了数据限制. 该方法有效地识别了手术仪器,即使是最少的训练示例,提高了手术场景的理解.
科学领域:
- 计算机视觉 计算机视觉
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 泛光细分对于理解外科手术场景至关重要,但由于高的注释成本和阶级不平衡,它面临着挑战.
- 针对特定的手术类别的有限数据阻碍了现有模型的性能.
研究的目的:
- 开发一个通用的几个镜头学习框架,用于在手术场景中准确的全光学细分.
- 为了应对手术数据集中有限的注释数据的挑战.
主要方法:
- 提出了一个三个阶段的框架,首先是微调一个稳定的扩散模型,用于多尺度表示的手术图像-文本对.
- 一个基于Mask2Former的解码器在基础课程上接受了培训,生成了面具提案.
- 引入了一种新的N-to-M口罩匹配方法,以使用有限的样本识别新的类别.
主要成果:
- 拟议的MM-former在有限的数据条件下,在新建的CholecPanSeg数据集上取得了出色的结果.
- 该方法在几次手术全视分段中表现出优越的性能,与以前的方法相比.
- 在一次通过中,可以准确识别新型类对象.
结论:
- 一般化的少数射击MM-former有效地处理类不平衡和手术全光学细分中的有限数据.
- 拟议的N-to-M口罩匹配方法可以对罕见的手术类别进行可靠的识别.
- 这一框架显著提升了手术场景的理解,并为改进人工智能辅助手术铺平了道路.
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