Spectral Unmixing to Reduce Refraction Effects in Feulgen-Stained Slides
Bibliographic record
Abstract
INTRODUCTION: As DNA image cytometry and DNA image histology serve as valuable tools in clinical tumor pathology, the need for precise and accurate DNA amount measurements is crucial. This study describes the process of employing spectral unmixing on Thionin-stained slides as a means of reducing refraction effects introduced in the image, during imaging, due to changes in the refraction index within the tissue being imaged. METHODS: A correction method that reduces refraction effects on the DNA quantitation measurements by making use of the spectrally limited absorption properties exhibited by Thionin relative to the more spectrally uniform effects of tissue refraction as a function of wavelength. RESULTS: Spectral unmixing enables an improved estimate of DNA amount at every pixel and a potentially truer representation of the actual distribution of the DNA within individual cell nuclei. CONCLUSIONS: Spectral unmixing is a valuable computational technique widely used in histology and cytology research. By reducing refraction-based optical artifacts in the image, it enhances the accuracy of DNA quantitation, minimizes variability, and improves the discriminating ability of nuclear DNA organization as quantified by texture features.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".