在使用ResNet嵌入式的低剂量CT上对肺气的可解释性诊断
Talshyn Sarsembayeva1, Madina Mansurova1, Ainash Oshibayeva2
1Faculty of Information Technologies and Artificial Intelligence, Department of Artificial Intelligence and Big Data, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan.
Journal of imaging
|January 27, 2026
概括
这项研究引入了一种深度学习管道,用于使用低剂量计算机断层扫描 (LDCT) 检测肺气. 可解释的框架实现了高精度,支持大规模查和人口健康研究.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 肺部医学 肺部医学
背景情况:
- 在低剂量计算机断层扫描 (LDCT) 上精确检测肺气对于查和人口研究至关重要.
- 现有的方法在解释性和可扩展性方面面临挑战.
研究的目的:
- 使用LDCT开发一种质量控制和可解释的深度学习管道,用于使用LDCT进行肺瘤评估.
- 通过将深度学习嵌入与定量CT标记器集成来提高诊断性能和稳定性.
主要方法:
- 利用ResNet-152深度学习模型从LDCT中肺补丁中提取特征.
- 实现了自动肺部细分,质量控制过和特征选择 (逻辑回归,LASSO,RFE).
- 化ResNet嵌入有定量CT (QCT) 标记 (%LAA,Perc15,TLV) 进行改进的分析.
主要成果:
- 实现了高诊断性能,ROC-AUC为0.996和PR-AUC为0.962.
- 证明了0.931的平衡精度,计算成本低.
- 验证了预先训练的ResNet嵌入的有效性,而不需要重新训练用于肺的特征.
结论:
- 拟议的管道提供了一个可复制和可解释的框架,用于在人口层面的最不发达国家分析中检测肺瘤.
- 这种方法支持研究,并作为肺肺复杂症的查支持工具.
- 深度学习嵌入,当与QCT标记器相结合时,显著提高了肺评估的稳定性和可解释性.
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