Improved scanning laser fundus imaging using polarimetry
Bibliographic record
Abstract
We present a polarimetric technique to improve fundus images that notably simplifies and extends a previous procedure [Opt. Lett.27, 830 (2002)]. A generator of varying polarization states was incorporated into the illumination path of a confocal scanning laser ophthalmoscope. A series of four images, corresponding to independent incoming polarization states, were recorded. From these images, the spatially resolved elements of the top row of the Mueller matrix were computed. From these elements, images with the highest and lowest quality (according to different image quality metrics) were constructed, some of which provided improved visualization of fundus structures of clinical importance (vessels and optic nerve head). The metric values were better for these constructed images than for the initially recorded images and better than averaged images. Entropy is the metric that is most sensitive to differences in the image quality. Improved visualization of features could aid in the detection, localization, and tracking of ocular disease and may be applicable in other biomedical imaging.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".