Label-Free Whole Slide Virtual Multi-Staining Using Dual-Excitation Photon Absorption Remote Sensing Microscopy
Bibliographic record
Abstract
Abstract Histochemical staining is essential for visualizing tissue architecture but is destructive and limited by tissue availability. Virtual staining with label-free microscopy offers a non-destructive alternative, enabling multiple stains from the same section. We present a dual-excitation Photon Absorption Remote Sensing (PARS) microscopy system, the first application of 355 nm UVA alongside 266 nm UVC excitation. The 355 nm source extends PARS contrast to include red blood cells, melanin, and enhanced stromal architecture through complementary radiative and non-radiative absorption. Using the RegGAN framework, we demonstrate the first PARS virtual staining across routine (H&E) and specialized (Masson’s trichrome, PAS, Jones methenamine silver) stains in human and murine tissues. Quantitative metrics show dual-excitation improves virtual stain similarity over single-wavelength inputs. A masked evaluation by expert pathologists provides an initial, coarse assessment indicating that virtual stains achieve ratings on a visual diagnostic quality scale comparable to chemical counterparts under limited evaluation conditions. These results support dual-excitation PARS as a promising non-destructive approach for multi-stain virtual histology. Corresponding whole-slide image pairs are available at the BioImage Archive ( https://doi.org/10.6019/S-BIAD2232 ).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.035 |
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".