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无标签的肺癌诊断由里埃变换红外显微镜学结合域对抗性学习

Yudong Tian¹, Xiangyu Zhao¹, Jingzhu Shao¹

¹Center for Biophotonics, Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. czwu@sjtu.edu.cn.

|June 10, 2025

概括

这项研究引入了一种新的富里埃变换红外光谱 (FTIR) 显微镜方法,与深度学习相结合,用于准确的肺癌诊断. 该技术增强了病理学分析,为早期检测和研究提供了有价值的工具.

科学领域:

生物医学工程生物医学工程
频谱学是一种光谱学.
计算病理学计算病理学

背景情况:

肺癌的诊断依赖于耗时的CT成像和组织病理学,容易出现错误.
目前的方法需要专家解释,并且可能是主观的.
像FTIR显微光谱学这样的无标签技术为组织提供了分子洞察力.

研究的目的:

开发一种先进的FTIR显微光谱法,以改善肺癌组织分化.
整合一个深度学习模型,以提高诊断准确度和克服患者间的变化.
建立FTIR显微镜作为肺癌病理学的补充工具.

主要方法:

利用富里埃变换红外光谱 (FTIR) 显微镜进行无标签组织分析.
开发了一个红外光谱域对抗神经网络 (IRS-DANN) 用于分类.
采用领域对抗性学习来解决光谱数据中患者间的变异性.

主要成果:

与IRS-DANN合作的新型FTIR显微光谱法在临床数据上实现了卓越的分类性能.
确切区分肺癌组织被证明,即使有有限的训练样本.
识别并可视化关键的FTIR指纹峰值与肺癌相关的特定生物成分相关.

结论:

FTIR显微镜与深度学习相结合,为肺癌诊断提供了一种强大的方法.
美国国税局-DANN模型有效地减轻了患者间的变化,提高了诊断可靠性.
这种综合技术显示出促进肺癌病理学研究和临床应用的巨大潜力.

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