Boosted decision trees for non-resonant background removal in hyperspectral CARS microscopy - John Shafe-Purcell; Aaron D Slepkov (JPhys Photonics, 2025)
Coherent anti-Stokes Raman scattering (CARS) is a nonlinear optical process used for spectroscopy and label-free chemical imaging. CARS signals can be orders of magnitude stronger than those of its incoherent counterpart, spontaneous Raman scattering, thus enabling substantially faster acquisition speeds. The presence of a pervasive non-resonant background (NRB) that distorts resonant peaks and introduces spurious signal to non-resonant spectral regions is the primary drawback that hinders spectral analysis and degrades chemical-selective image contrast in CARS microscopy. NRB removal techniques that retrieve Raman-like signals from CARS spectra have thus long been a central focus of CARS research, with ‘deep learning’ computational approaches of increasing complexity being most recently explored. Here, we present an alternative ‘shallow’ machine learning approach to NRB removal, using tree-based gradient boosting with XGBoost. We find that the gradient-boosted decision trees accurately retrieve Raman-like lineshapes in simulated CARS spectra, and when applied to experimental hyperspectral CARS images, the gradient-boosted decision trees significantly improve chemical-selective contrast. This work establishes tree-based gradient boosting as a rapid and effective tool for NRB removal in hyperspectral CARS microscopy, and thus challenges the need to apply approaches of ever-increasing computational complexity.