Fluorescence angular domain imaging of skin tissue phantoms using intralipid-infused solids
Bibliographic record
Abstract
Optical imaging through biological tissue has the significant problems of scattering which degrades the image resolution and quality. Research has shown that Angular Domain Imaging (ADI) improves image quality by filtering out the scattered light in the biological tissue images based on the angular direction of photons. The advantage of this technique is that it is independent of the wavelength, coherent, pulse, or duration compared to OCT or time domain. This allows us to couple ADI with conventional fluorescence imaging technique. Previous work was creating test media by varying Intralipid/water concentration to produce different scattering levels. This showed difficulties in producing a consistent scattering medium in liquid states. Hence, ideally we want a reusable solid medium which has a stable scattering characteristic. Our target is to investigate fluorescence ADI on skin with cancerous collagen tissue where healthy collagen fluoresces while the cancerous collagen tissue does not. To mimic the characteristic of skin, a solid scattering medium over a patterned fluorescence material with non-emitting structures is created. We used a solid agar medium, or a transparent polymer, infused with Intralipid at different concentrations, as the scattering medium. The solid media with similar scattering characteristic of skin (μs = 20cm-1, g = 0.85) is placed on top of a fluorescence plastic (415nm excitation, ≈ 530nm emission) which is patterned by strips of non-emitting structures (200-400μm). Using small apertures with acceptance angles of 0.171° a distance away from the solid scattering medium, these non-emitting structures are detectable at shallow scattering tissue depth (1-2mm).
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".