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用干扰过和动态不确定性挖矿的多片细分

Yunhua Zhang^1,2, Gang Yang¹, Congjin Gong¹

¹Northeastern University, Shenyang 110819, People's Republic of China.

Physics in medicine and biology

|February 21, 2024

概括

这项研究引入了一种使用预处理子网络和动态不确定性挖掘网络的两阶段方法,用于在结肠镜图像中准确的聚细分,从而改善早期结肠直肠癌检测.

科学领域:

医疗成像医学成像
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

准确的聚细分对于早期结直肠癌诊断至关重要.
图像噪声和模两可的界限挑战了现有的细分方法.
改善聚细分性能对于临床决策至关重要.

研究的目的:

开发一种新的两阶段方法,用于精确的多片细分.
为了应对图像噪声和不清楚的息肉边界所带来的挑战.
为了提高多片细分模型的准确性和概括性.

主要方法:

这是一个两阶段的方法,结合了预处理子网络 (Pre-Net) 和动态不确定性采矿网络 (DUMNet).
在结肠镜图像中,Pre-Net可以过干扰区域.
DUMNet使用一个不确定性挖掘模块 (UMM) 来进行粗细细分段,重点关注像素保密度.

主要成果:

拟议的方法在五个基准数据集 (ETIS,CVC-ClinicDB,CVC-ColonDB,EndoScene,Kvasir) 上实现了最先进的性能.
预网证明了可移植性,提高了现有细分模型的准确性.
通过消除干扰和挖掘不确定的区域,DUMNet有效地改善了细分.

结论:

这种新的两阶段方法显著提高了多片细分的准确性.
这一进步有助于临床医生准确诊断,并降低结直肠癌的风险.
开发的Pre-Net提供了一种多功能工具,用于增强当前的多重体细分技术.

关键词:

结肠镜检查结肠镜检查医疗图像细分医疗图像细分聚合物细分的聚合物细分在加工前进行预处理.

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