Automated Screening for Diabetic Retinopathy Using Compact Deep Networks
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".