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基于转移学习的双重成像方法的整合,以提高肺癌共聚焦激光内显微镜的分类精度

Mircea-Sebastian Șerbănescu¹, Liliana Streba², Alin Dragoș Demetrian³

¹Department of Medical Informatics and Statistics, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania.

|February 26, 2025

概括

使用组织学和PCLE图像的双转移学习 (TL) 显著提高了肺癌分类准确度. 这种方法提高了诊断能力,以获得更好的患者结果.

科学领域:

医疗成像医学成像
在瘤学瘤学.
人工智能的人工智能

背景情况:

肺癌是癌症死亡的主要原因,需要先进的诊断工具.
聚焦激光内分显微镜 (pCLE) 显示出在体内组织分析的前景.
目前的PCLE图像分类方法需要改进.

研究的目的:

用PCLE图像来提高肺癌分类的准确性.
评估一个双转移学习 (TL) 方法,整合组织学和PCLE数据.
将双TL与常规TL进行比较,用于PCLE图像分析.

主要方法:

收集了40名肺癌患者的PCLE图像和组织样本.
开发了800张良性图像和800张恶性图像的平衡数据集.
应用了三种CNN架构 (AlexNet,GoogLeNet,ResNet),使用双TL (组织学然后PCLE) 和共聚焦TL (仅PCLE).
使用精度和AUC评估模型性能,在50次运行中进行10倍交叉验证.

主要成果:

在所有测试的CNN架构中,双TL显著优于对焦TL.
拥有双TL的AlexNet实现了94.97%的准确性和0.98 AUC.
双TL模型显示了统计学上显著的性能改善 (p < 0.001).
通过双TL模型观察到较少的假阳性和假阴性.

结论:

通过双TL整合组织学和PCLE成像,显著改善了肺癌的分类.
这种双TL方法为增强组织分类提供了一个强大的框架.
该方法有可能改善肺癌诊断患者的治疗结果.

关键词:

分类的准确性分类的准确性.同焦点的激光内微镜.深度学习是一种深度学习.双重转移学习是双重转移学习.组织学成像检查肺癌是一种肺癌.医学成像医学成像多式联运集成多式联运集成转移学习转移学习

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