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一个不确定性意识的自我训练框架与一致性规范化用于多标签分类的常见的计算机断层扫描在肺结节的迹象

Ketian Zhan¹, Yunpeng Wang², Yaoyao Zhuo³

¹Academy for Engineering & Technology, Fudan University, Shanghai, China.

Quantitative imaging in medicine and surgery

|September 15, 2023

概括

这项研究引入了一种新的系统,用于使用3D卷积神经网络 (CNN) 和半监督学习 (SSL) 来分类肺结节的迹象. 该系统通过准确地分类结节的迹象,即使有有限的标记数据,也有效地提高了诊断效率.

科学领域:

医疗成像医学成像
人工智能的人工智能
放射学放射学是一门学科.

背景情况:

肺结节的计算机断层扫描 (CT) 标志对于评估恶性瘤至关重要.
准确的自动分类这些迹象可以提高诊断效率.
有限的标签数据和少数关于多标签分类 (MLC) 的研究阻碍了该领域的研究.

研究的目的:

为肺结节的症状开发一个多标签的自动分类系统.
为应对医疗图像分析中标记数据不足的挑战.
为了提高肺结节标志分类的准确性和效率.

主要方法:

提出了一个系统,将一个三维 (3D) 卷积神经网络 (CNN) 与一个不确定性意识的自我训练框架与一致性规范化 (USC) 结合起来.
该系统通过监督和半监督学习 (SSL) 使用了标记和未标记的数据.
对肺结节分析16 (LUNA16) 公共数据集和私人数据集进行了实验.

主要成果:

对于MLC的结节标志,3D CNN实现了0.870的平均平均精度 (mAP) 和0.782的曲线下的平均面积 (AUC).
与监督学习相比,拟议的SSL方法使mAP增加了7.6% (至0.806),平均AUC增加了8.1% (至0.712).
通过有效利用未标记的数据,SSL方法展示了卓越的性能改进.

结论:

开发的3D CNN有效地进行了肺结节征兆的多标签分类 (MLC).
拟议的SSL方法为3D医疗图像MLC任务中有限的标记数据提供了有效的解决方案.
这种方法提高了肺结节分析的诊断效率和准确性.

关键词:

三维卷积神经网络 3D卷积神经网络计算机断层扫描显示的标志计算机断层扫描 (CT) 是一种计算机断层扫描.多标签分类 (MLC) 是指多标签分类.半监督学习 (SSL) 是指半监督学习.

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