Spatiofrequency filters for imaging fluorescence in scattering media
Bibliographic record
Abstract
Researchers have been using simple optics to image optically induced fluorescence in tissues. We now apply the Angular Domain Imaging technique using a Spatiofrequency filter which accepts only photons within a small deviation angle from its original trajectory to image a fluorescing medium beneath a scattering layer. A Rhodamine 6 G dye fluorescing layer or fluorescence slides, under an Intralipid scattering medium was used. By applying ADI with acceptance angle of 0.17°, the structures are distinguishable at low scattering depth depending of the emission wavelength of the fluorescence source. It was established previously that as the acceptance angle increases, the amount of scattered light/noise in the images increases, however, the resolution also deteriorates. Simulations using a Monte-Carlo program are done for both angular filters, Spatiofrequency filter and Linear Collimating Array. Due to the additional positional filtration on top of the angular filtration with Linear Collimating Array, collimators with aspect ratio as low as 10:1 can improve the quality of the fluorescence images significantly in both contrast ratio and resolution.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".