Proliferative diabetic retinopathy characterization based on the spatial organization of vascular junctions in fundus images
Bibliographic record
Abstract
Proliferative diabetic retinopathy is an important public health issue with deteriorating impact on the vision of its patient. In this study, a novel approach is proposed for the characterization of abnormal vessels based on a spatial point pattern method. Points of interest corresponding to vascular junctions are detected by a perceptual organization technique, and then second-order statistical measures are computed. Significant differences (p<;0.05) between healthy retinal regions and areas with neovascularizations were obtained, which suggests that the second-order statistics could be used as a relevant feature to discriminate the abnormal from the normal vasculature. The relevance of the new measures was also evaluated with respect to an existing set of features using classification. The inclusion of a new second-order measure increases the characterization sensitivity against the already existing features from 75.76% to 84.85%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".