TriFNet:一个三分支的特征融合网络,用于通过表面增强的拉曼光谱来确定pH值
Zheng Zhao1, Ziyi Jin2, Guoqing Wu1
1School of Information Science and Technology, Fudan University, Shanghai 200438, China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|February 22, 2024
概括
使用拉曼光谱和新型深度学习网络 (TriFNet) 精确的pH检测有助于精确的瘤手术. 这种方法显著改善了瘤微环境中的pH值测定现有技术.
科学领域:
- 生物医学工程 生物医学工程
- 分析化学 分析化学
- 计算生物学 计算生物学
背景情况:
- 由于癌细胞的代谢转变,导致的酸性瘤微环境,需要精确的细胞外流体pH检测才能有效切除瘤.
- 拉曼光谱与深度学习相结合,为pH检测提供了一种有前途的方法,但使用1D-CNN的当前方法受限于特征提取能力.
研究的目的:
- 开发和验证一种新的2D深度学习模型,即三分支特征融合网络 (TriFNet),用于使用表面增强拉曼光谱 (SERS) 精确的pH测定.
- 通过将拉曼光谱转换为多个图像格式并使用注意力融合模块来增强从光谱数据中提取特征.
主要方法:
- 开发一个二维三分支网络 (TriFNet),将SERS数据转换为三种图像类型,用于全面的模式识别.
- 集成注意力融合模块,有效地结合空间和通道特征,捕获补充信息以改进pH值的确定.
- 在14137个SERS样本的数据集上进行验证.
主要成果:
- 在pH测定中,TriFNet模型实现了高精度,平均绝对误差 (MAE) 为0.059,绝对误差 (SD) 的标准偏差为0.07,根平均平方误差 (RMSE) 为0.092,R2为0.991.
- 与现有方法相比,性能显著改善,平均指标改进率为47% (MAE),39% (SD),43% (RMSE) 和6% (R2).
- 模型可视化证实了其诊断能力和与生物分子特征的相关性,证明了不同SERS芯片的稳定性.
结论:
- 拟议的TriFNet模型提供了使用SERS在瘤微环境中准确确定pH值的优越方法.
- 这种先进的深度学习技术显示出开发基于拉曼光谱的有效工具以指导外科手术的巨大潜力.
- 该方法的稳定性和提高的准确性突出了其在精密瘤学中的临床适用性.
相关概念视频
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