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

Author:

Yao Bowen1,Lin Fangrui1,Luo Ziyi1,Chen Qinglin1,Lin Danying1,Yang Zhigang1,Li Jia1ORCID,Qu Junle1

Affiliation:

1. State Key Laboratory of Radio Frequency Heterogeneous Integration Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen 518060 China

Abstract

AbstractCoherent 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.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province

Publisher

Wiley

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