Toward noncontact macroscopic imaging of multiple cancers using multi‐spectral inelastic scattering detection
Bibliographic record
Abstract
Abstract Here we introduce a Raman spectroscopy approach combining multi‐spectral imaging and a new fluorescence background subtraction technique to image individual Raman peaks in less than 5 seconds over a square field‐of‐view of 1‐centimeter sides with 350 micrometers resolution. First, human data is presented supporting the feasibility of achieving cancer detection with high sensitivity and specificity – in brain, breast, lung, and ovarian/endometrium tissue – using no more than three biochemically interpretable biomarkers associated with the inelastic scattering signal from specific Raman peaks. Second, a proof‐of‐principle study in biological tissue is presented demonstrating the feasibility of detecting a single Raman band – here the CH 2 /CH 3 deformation bands from proteins and lipids – using a conventional multi‐spectral imaging system in combination with the new background removal method. This study paves the way for the development of a new Raman imaging technique that is rapid, label‐free, and wide field.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".