Boosted decision trees for non-resonant background removal in hyperspectral CARS microscopy
Bibliographic record
Abstract
Abstract 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".