Automated Screening for Diabetic Retinopathy Using Compact Deep Networks
Why this work is in the frame
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Bibliographic record
Abstract
Diabetes is a chronic condition affecting millions of people worldwide.One of its major complications is diabetic retinopathy (DR),which is the most common cause of legal blindness in the developedworld. Early screening and treatment of DR prevents visiondeterioration, however the recommendation of yearly screening isoften not being met. Mobile screening centres can increasing DRscreening, however they are time and resource intensive becausea clinician is required to process the images. This process can beimproved through computer aided diagnosis, such as by integratingautomated screening on smartphones. Here we explore the useof a SqueezeNet-based deep network trained on a fundus imagedataset composed of over 88,000 retinal images for the purpose ofcomputer aided screening for diabetic retinopathy. The results ofthis neural network validated the viability of conducting automatedmobile screening of diabetic retinopathy, such as on a smartphoneplatform.
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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 | 0.001 | 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 | 0.000 |
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 it