Boosted decision trees for non-resonant background removal in hyperspectral CARS microscopy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it