通过特征规范化增强开放世界的细菌拉曼光谱识别,以提高对未知类的抗性.
Yaroslav Balytskyi1, Nataliia Kalashnyk2, Inna Hubenko3
1Department of Physics and Astronomy, Wayne State University, Detroit, Michigan 48201, United States.
使用拉曼光谱的深度学习可以识别细菌,但与未知的病原体作斗争. 这项研究引入了一种新的方法,使用对象层损失来准确识别已知的细菌,并有效地标记未知的细菌,减少错误阳性.
科学领域:
- 微生物学 微生物学
- 频谱学是一种光谱学.
- 人工智能的人工智能
背景情况:
- 深度学习和拉曼光谱在临床环境中提供了快速的细菌识别.
- 传统的封闭式模型在未知或新出现的病原体上失败,导致高错误阳性率.
- 当前的神经网络容易受到不可预测的临床环境和未知的微生物输入的影响.
研究的目的:
- 开发一个强大的深度学习模型,使用拉曼光谱来准确和可靠地识别病原体.
- 通过有效处理未知的细菌样本来解决封闭集分类的局限性.
- 降低病原体检测中的错误阳性率,提高适应新出现的微生物威胁的能力.
主要方法:
- 开发了一组包含注意力机制的ResNet架构.
- 集成的特征规范化使用对象圈损失函数用于改进分类.
- 评估模型在识别已知的病原体和检测未知的样本方面的性能.
主要成果:
- 在已知病原体识别方面,达到87.8 ± 0.1%的30个隔离精度.
- 有效地分离未知样本,显著降低了假阳性率.
- 证明了分布外探测器的增强性能,以改善未知类检测.
结论:
- 开发的算法通过拉曼光谱学增强了已知和未知的病原体的识别.
- 该方法确保了对未来新出现的病原体的适应性,增加了诊断可靠性.
- 该方法可以扩展到在动态环境中改进开放式医学图像分类.
更多相关视频
11:09Use of MALDI-TOF Mass Spectrometry and a Custom Database to Characterize Bacteria Indigenous to a Unique Cave Environment Kartchner Caverns, AZ, USA
Published on: January 2, 2015
09:32Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach
Published on: September 26, 2019
相关概念视频
Raman Spectroscopy: Overview
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
Raman Spectroscopy Instrumentation: Overview
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
Methods of Classification and Identification
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)
IR Frequency Region: Fingerprint Region
Special Staining Techniques
