自主监督学习用于通用拉曼频谱否定
Siyi Wu1, Yumin Zhang1, Chang He1
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
Analytical chemistry
|October 23, 2024
概括
本研究介绍了一种自我监督的深度学习算法,用于消除拉曼和表面增强拉曼散射 (SERS) 频谱. 该方法准确且快速地减少噪音,改善光谱分析和瘤检测等应用.
科学领域:
- 频谱学是一种光谱学.
- 光学传感传感器是什么?
- 生物医学光学 生物医学光学
背景情况:
- 拉曼和SERS光谱具有高的特异性和灵敏度,但对于弱信号需要有效的降噪.
- 目前的消毒方法通常是手动的,耗时的,并且可能不会产生最佳的结果.
- 深度学习方法用于光谱无声化通常需要广泛的标记数据,这往往是不可用的.
研究的目的:
- 开发一种通用,自我监督的学习算法,用于准确,快速和强大的拉曼和SERS光谱的否定.
- 为了克服手动参数优化的局限性和在光谱无声化中需要大型标记数据集的需求.
- 在具有挑战性的应用中提高拉曼和SERS光谱的性能和可解释性.
主要方法:
- 一个基于U-Net的深度学习网络被设计用于特征提取和光谱峰值恢复.
- 实施了一种新的亚取样策略,以精制光谱并减轻偏差干扰.
- 用自主监督学习来消除在培训期间对真实光谱标签的要求.
主要成果:
- 该算法在多样化的光谱数据集中表现出强大的概括性,超过了最先进的拒绝技术.
- 在光安全的瘤检测中,信号与噪声比提高了400%以上,检测深度从10厘米增加到18厘米.
- 封闭分析证实了算法的重点是特征的光谱峰值,提高了可解释性.
结论:
- 拟议的自我监督的除算法为拉曼和SERS光谱学提供了准确,快速和强大的解决方案.
- 这种方法显著提高了光谱质量,并扩大了生物医学应用中的光学传感能力.
- 该算法的可解释性和卓越性能为光谱学领域提供了宝贵的进步.
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