在飞行中的拉曼显微镜保证了歧视的准确性.
Koji Tabata1,2, Hiroyuki Kawagoe3, J Nicholas Taylor1
1Research Center of Mathematics for Social Creativity, Research Institute for Electronic Science, Hokkaido University, Sapporo 001-0020, Hokkaido, Japan.
概括
机器学习加速拉曼显微镜测量以进行样本歧视. 这种技术显著减少了所需的照明点,为医学诊断等应用程序提供了更快,更准确的分析.
科学领域:
- 显微镜的使用方法
- 机器学习 机器学习
- 频谱学是一种光谱学.
背景情况:
- 自发拉曼显微镜提供了无标签的化学信息,但由于小的散射横截面,由于长时间的获取时间而受到限制.
- 加快测量对于成本和时间限制的应用,如细胞表型分类,至关重要.
研究的目的:
- 使用机器学习开发拉曼显微镜的加速成像技术.
- 设计一种适应性照明策略,优化测量点,以获得更快的辨别精度.
主要方法:
- 开发了一种基于机器学习 (ML) 的成像技术,利用强化学习.
- 实施了自适应反系统,以确定测量期间的"最佳"照明模式.
- 在人类卵泡甲状腺和癌细胞拉曼图像以及聚合物珠混合样本上验证了该技术.
主要成果:
- 该ML技术所需的照明比拉斯特扫描少3,333至31,683倍,用于细胞表型歧视.
- 对于样本条件的区分,该系统使用的照明比标准点点照明拉曼显微镜少104到4350倍.
- 根据所需的分辨准确性,对照明点的量化评估.
结论:
- 拟议的ML算法显著加速拉曼显微镜测量,同时保持差别精度.
- 适应性照明策略为各种应用中,包括医学诊断中,提供了更快,更准确的测量途径.
- 该技术适用于其他显微镜方法,允许在飞行中的测量条件控制.
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