Polarización y oftalmología confocal
Bibliographic record
Abstract
espanolLa incorporacion de un polarimetro a un oftalmoscopio confocal de barrido laser ha dado lugar a una herramienta que no solamente proporciona una resolucion espacial de las propiedades de polarizacion de la retina, sino que tambien permite la mejora de las imagenes del fondo del ojo registradas en base a diferentes metricas de calidad de imagen. Esta optimizacion puede aplicarse tanto a la totalidad de la imagen como a zonas determinadas de especial interes. Asi, tanto la tecnica como el instrumento podrian ser utiles en entornos clinicos orientados a la deteccion precoz de patologias retinianas y a su seguimiento. En este trabajo se resumen los resultados obtenidos en esta linea, de las colaboraciones entre el Laboratorio de Optica de la Universidad de Murcia y el Campbell?s Lab de la Universidad de Waterloo en Canada durante los ultimos 6 anos. EnglishThe combination of a polarimeter and a confocal scanning laser ophthalmoscope provides with a tool to investigate not only the spatially resolved retinal polarization properties, but also a method to improve the quality of fundus images based on different image quality metrics. This optimization can be applied either to the whole retinal image or to local features of relevant interest. Then, both the technique and the instrument might be useful in clinical environments for early detection of retinal pathologies or the following-up. Along this work some results on this issue will be summarized. These are a result of a collaboration between the Laboratorio de Optica at the Universidad de Murcia and the Dr. Campbell’s Lab at the University of Waterloo in Canada during the last 6 years..
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".