The Foveal Avascular Zone Image Database (FAZID)
Bibliographic record
Abstract
The Foveal Avascular Zone (FAZ) is of clinical importance since the vascular arrangement around the fovea changes with disease and refractive state of the eye. Therefore, it is important to segment and quantify the FAZ accurately. Studies done to date have achieved reasonable segmentation but there is a need for considerable improvement. In order to test and validate newly developed automated segmentation algorithms, we have created a public dataset of these retinal fundus images. The 304 images in the dataset are classified into: diabetic (107), myopic (109) and normal (88) eyes. The images were classified by a clinical expert and include clinical grading of diabetic retinopathy and myopia. The images are of dimensions 420 x 420 pixels (6mm x 6mm of retina). Both clear and manually segmented by a clinical expert (ground truth) images are available (608 total images). In these images, the FAZ is the green region marked in manually segmented image. The images can be used to test newly developed techniques and the manual segmentation images can be used as a ground truth for making performance comparisons and validation. It should also be noted there are only a few studies using supervised learning to segment the FAZ and this dataset will potentially be useful for machine learning training and validation. The image database, The Foveal Avascular Zone Image Database (FAZID) dataset can be accessed from the ICPSR website at the University of Michigan (https://doi.org/10.3886/E117543V2).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".