Machine learning empowered coherent Raman imaging and analysis for biomedical applications.

ABSTRACT: In situ and in vivo visualization and analysis of functional, endogenous biomolecules in living systems have generated a pivotal impact in our comprehension of biology and medicine. An increasingly adopted approach involves the utilization of molecular vibrational spectroscopy, which delivers notable advantages such as label-free imaging, high spectral density, high sensitivity, and molecule specificity. Nonetheless, analyzing and processing the intricate, multi-dimensional imaging data to extract interpretable and actionable information poses a fundamental obstacle. In contrast to conventional multivariate methods, machine learning has recently gained considerable attention for its capability of discerning essential features from massive datasets. Here, we present a comprehensive review o

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC11761466 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature