The Foveal Avascular Zone Image Database (FAZID)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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 it