Recent Progress in Deep Learning for Improving Coherent Anti-Stokes Raman Scattering Microscopy

Abstract

Coherent anti-Stokes Raman scattering (CARS) microscopy is a powerful label-free imaging technique that leverages biomolecular vibrations and is widely used in different fields. However, its intrinsic non-resonant background (NRB) can distort Raman signals and compromise spectral fidelity. Conventional data analysis methods for CARS encounter a bottleneck in achieving high accuracy. Furthermore, CARS requires balancing imaging speed against image quality. In recent years, endeavors in deep learning have effectively overcome these obstacles, advancing the development of CARS. This review highlights the research that applies deep learning to mitigate NRB, classify CARS data for disease identification, and denoise images. Each approach is delineated in terms of network architecture, training data, and loss functions. Finally, the challenges in this field is discussed and using the latest deep learning advancement is suggested to enhance the reliability and efficiency of CARS microscopy.