肺结节的人类形态诊断系统使用基于弱注释的深度学习
Lipeng Xie1, Yongrui Xu2, Mingfeng Zheng2
1School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, China.
概括
这项研究引入了一种深度学习系统,用于使用弱注释对肺结节 (PN) 进行分类,从而在肺癌检测中实现高精度. 该系统有效地定位和诊断PN,提供一个可行的临床工具.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 在瘤学瘤学.
背景情况:
- 在CT扫描中精确的肺结节分类对于早期肺癌的检测和诊断至关重要.
- 目前的结节分类方法需要大量高质量的注释,限制了临床适用性.
- 区分结节质量和纹理有助于在结节管理中的临床决策.
研究的目的:
- 开发一种基于深度学习 (DL) 的系统,用于使用弱注释数据进行肺结节 (PN) 诊断.
- 为了实现与全注释系统可比的性能,同时减少注释负担.
- 为了使PNS的有效局部化和差异诊断,包括各种亚型.
主要方法:
- 开发一个DL系统的PN分类 (良性与恶性) 训练在弱注释.
- 使用球尺度转换技术整合手工制作的形状特征.
- 在LIDC-IDRI和内部数据集上进行5倍交叉验证.
主要成果:
- 在LIDC-IDRI数据集 (814例) 上,在PN局部化方面达到0.938的曲线下面面积 (AUC),在差异诊断方面达到0.912 (814例).
- 在内部数据集 (822例) 上,在PN局部化方面达到0.943的AUC,在差异诊断方面达到0.815的AUC.
- 证明有能力通过各种标签来区分PN,例如磨砂玻璃,部分固体和固体结节.
结论:
- 开发的DL系统有效地通过使用弱注释定位和诊断肺结节.
- 该系统的性能与全注释系统相美,解决了当前方法的局限性.
- 该系统在资源有限的环境中的效率表明了肺癌诊断中的临床转化潜力.
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