Glaucoma diagnosis using transfer learning methods
Bibliographic record
Abstract
Comparison of deep learning results from various studies for glaucoma diagnosis is essentially meaningless since private data sets are often used. Another challenge is overfitting of the deep learning models with relatively small public datasets. This overfitting leads to poor generalization. Here, we propose a practical approach for fine tuning an existing state-of-the art deep learning model, namely, the Inception-v3 for glaucoma detection.. A two pronged approach using a transfer learning methodology combined with data augmentation and normalization is proposed herein. We used a publicly available dataset, RIM-ONE which has 624 monocular and 159 stereoscopic retinal fundus images. Data augmentation operations mimicking the natural deformations in fundus images along with Contrast Limited Adaptive Histogram Equalization (CLAHE) and normalization were applied to the images. The weights of Inception-v3 network were pretrained on the ImageNet dataset which consists of real-world objects. We finetuned this network for the RIM-ONE dataset to get the deep features required for glaucoma detection without overfitting. Even though we used a small dataset, the results obtained from this network are comparable to that reported in the literature.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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 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".