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使用特征金字塔形网络进行自我监督的表现学习,用于结肠直肠多检测.

Tianyuan Gan¹, Ziyi Jin¹, Liangliang Yu²

¹Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.

Scientific reports

|December 8, 2023

概括

一种新的自我监督学习方法,特征金字塔族网络 (FPSiam),通过未标记的结肠镜数据改善了结肠直肠多的检测. 这种方法减少了对专家注释的需求,提高了癌症查的诊断准确性和效率.

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科学领域:

医疗成像医学成像
人工智能在医学中的应用
胃肠病学胃肠病学

背景情况:

结肠直肠癌是一个重大的全球健康问题,早期检测对于改善患者的治疗结果至关重要.
计算机辅助诊断 (CAD) 系统,特别是使用卷积神经网络的系统,在结肠镜检查期间对实时的息肉检测充满希望.
当前的CAD方法通常需要广泛的,专家注释的数据集,这对开发和广泛采用构成了障碍.

研究的目的:

引入一种新的自我监督的表示学习方法,特征金字塔罗网络 (FPSiam),用于结肠直肠多检测.
利用大量未标记的结肠镜数据集来克服手动注释的局限性.
在资源有限的场景和有限的标记数据中增强聚合物检测系统的性能.

主要方法:

开发了一个特征金字塔编码器,从结肠镜图像中提取和融合本地和全球特征.
使用自主监督学习的米网络,从未标记的数据中获取一般视觉特征表示.
将学到的特征转移到下游的结肠直肠多检测任务中,使用标准检测器.

主要成果:

拟议的FPSiam方法在结直肠聚检测方面取得了最佳的性能.
FPSiam的表现优于其他最先进的自我监督学习方法.
与转移学习方法相比,FPSiam表现出优异的结果,在两种典型探测器中实现了2.3 mAP和3.6 mAP的更高.

结论:

FPSiam为开发强大的结直肠多检测系统提供了成本效益高的解决方案,特别是当标记数据稀缺时.
该方法有效地利用未标记的结肠镜检查数据,减少对劳累性的专家注释的依赖.
FPSiam为推进各种内镜图像分析任务提供了一个有前途的方法,超出了聚合物检测.