Glaucoma diagnosis using transfer learning methods
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
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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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) | 0.002 | 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