Comparative study of differential matrix and extended polar decomposition formalisms for polarimetric characterization of complex tissue-like turbid media
Bibliographic record
Abstract
Development of methodologies for quantification/unique interpretation of the intrinsic polarimetry characteristics of biological tissues are important for various applications involving tissue characterization/diagnosis. A detailed comparative evaluation of the polar decomposition and the differential matrix decomposition of Mueller matrices for extraction/quantification of the intrinsic polarimetry characteristics (with special emphasis on linear retardance δ, optical rotation Ψ and depolarization Δ parameters was performed, because these are the most prominent tissue polarimetry effects) from complex tissue-like turbid media exhibiting simultaneous scattering and polarization effects. The results suggest that for media exhibiting simultaneous linear retardance and optical rotation polarization events, the use of retarder polar decomposition with its associated analysis which assumes sequential occurrence of these effects, results in systematic underestimation of δ and overestimation of Ψ parameters. Analytical relationships between the polarization parameters (δ, Ψ) extracted from both the retarder polar decomposition and the differential matrix decomposition for either simultaneous or sequential occurrence of the linear retardance and optical rotation effects were derived. The self-consistency of both decompositions is validated on experimental Mueller matrices recorded from tissue-simulating phantoms (whose polarization properties are controlled, known a-priori, and exhibited simultaneously) of increasing biological complexity. Additional theoretical validation tests were performed on Monte Carlo-generated Mueller matrices from analogous turbid media exhibiting simultaneous depolarization (Δ), linear retardance (δ) and optical rotation (Ψ) effects. After successful evaluation, the potential advantage of the differential matrix decomposition over the polar decomposition formalism was explored for monitoring of myocardial tissue regeneration following stem cell therapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".