一种基于时间卷积网络的方法和对结肠镜视频时间细分的基准数据集
Carlo Biffi1, Giorgio Roffo1, Pietro Salvagnini1
1Cosmo Intelligent Medical Devices, Dublin, Ireland.
Computer methods and programs in biomedicine
|July 9, 2025
概括
本研究介绍了ColonTCN,这是一种新的深度学习模型,用于将结肠镜视频细分为解剖部分和程序阶段. 开发的开放访问数据集和ColonTCN模型推进了自动化结肠镜报告和计算机辅助诊断.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 人工智能在医学中的应用
背景情况:
- 结肠镜的计算机辅助检测和诊断系统的进步正在推动对自动报告的需求.
- 将结肠镜视频精确划分为解剖部分和手术阶段对于开发这些系统至关重要.
- 现有的研究还没有充分解决为此特定的时间细分任务创建数据集和模型的问题.
研究的目的:
- 创建第一个开放访问数据集,用于全程序结肠镜视频的时间细分.
- 为此细分任务提出和评估一种最先进的深度学习方法,ColonTCN.
- 将ColonTCN与竞争模式进行比较,并提供对结肠镜视频细分挑战的见解.
主要方法:
- 标注了REAL-Colon数据集 (270万,60个视频) 与解剖位置和程序阶段的级标签.
- 开发了ColonTCN,这是一个设计用于在视频中高效的时间依赖性捕获的时间卷积网络架构.
- 实施了一种双k倍交叉验证协议,用于对未见的多中心数据进行可靠的模型评估.
主要成果:
- 科隆TCN在参数数量低的情况下实现了最先进的分类准确性.
- 该模型在k-fold交叉验证设置中都超过了竞争方法.
- 废弃研究证实了定制时卷积块在提高学习和模型效率方面的有效性.
结论:
- 拟议的开放访问基准和ColonTCN代表了对结肠镜手术时间细分的重大进步.
- 这项工作促进了进一步的开放访问研究,以满足对自动化结肠镜分析的临床需求.
- 该代码和数据集是公开可用的,以支持研究界.
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