快速:卷积自编码器用于瘤细胞和组织拉曼光谱的自动预处理
Applied spectroscopy
|February 2, 2026
概括
一个新的深度学习模型自动化了用于癌症放射治疗研究的拉曼光谱 (RS) 数据预处理. 这种卷积自编码器 (AE) 有效地从瘤细胞和组织光谱中去除器件,改进了对辐射反应的分析.
科学领域:
- 生物医学光学 生物医学光学
- 计算生物学 计算生物学
- 频谱学是一种光谱学.
背景情况:
- 拉曼光谱 (RS) 提供无标签的分子分析,用于分析瘤对放射治疗的反应.
- 有效的光谱预处理对于准确的RS数据分析至关重要,涉及基线减去,平滑和文物校正.
- 目前的预处理方法可能耗时,需要人工干预.
研究的目的:
- 开发一种单步,自动化的光谱预处理方法,用于从瘤细胞和组织中获取拉曼光谱数据.
- 评估卷积自编码器 (AE) 在去除光谱工件和改善放射治疗研究数据质量的性能.
- 评估AE在识别低质量的光谱中对半自动异常值检测的有用性.
主要方法:
- 卷积式自编码器 (AE) 架构用于自动化光谱预处理.
- 训练了两个AE模型:一个用于临床前 (细胞系,异种移植),一个用于临床 (前列腺活检) 拉曼光谱.
- 与基线算法相比,使用根平均平方误差 (RMSE) 和百分比根平均平方差 (PRD) 量化了AE的性能.
- 一个重建AE被训练为半自动识别质量差的光谱.
主要成果:
- 该AE证明了从临床前和临床频谱中快速有效地去除基线,噪声和宇宙射线 (CRs).
- 对于临床前数据,AE实现了7.1 × 10-5的RMSE和3.1%的PRD,去除了94.0%的CRs.
- 对于临床数据,AE实现了8.1 × 10-5的RMSE和3.7%的PRD,去除了90.2%的CRs.
- 在没有GPU的情况下,AE在2.4秒内处理了约11,000个光谱.
- 一个重建的AE实现了96.4%的协议与手动异常值的识别.
结论:
- 开发的深度学习框架为在辐射反应研究中预处理瘤拉曼光谱提供了高效和自动化的解决方案.
- AE显著提高了数据质量,使生物化学辐射反应概况的一致提取成为可能.
- 这种自动化方法为临床应用提供了拉曼光谱数据的大规模分析.
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