Use of Convolutional Neural Network for Fully Automated Segmentation of Hard Exudates in Retinal Images
Bibliographic record
Abstract
Diabetic retinopathy (DR), is a complication with diabetes caused by damaged blood vessels in the back of the retina. DR affects 126.6 million people around the world and is the leading cause of blindness. Hard exudates are a type of lesion caused by the damaged blood vessels and are an early marker for DR. In this research, a fully automatic deep learning method has been developed that is able to delineate hard exudate lesions in retinal images. This allows the lesion volume to be calculated and thus determine DR severity. This technology would remove the need for doctors in the diagnosis process, therefore making the diagnosis faster and more accessible to people around the world. Our dataset consisted of 58 images and was used to train a fully convolutional neural network with a U-net architecture. The U-net consists of a contracting path followed by a symmetric expansive path that was used to learn features of the images. These features were then used to differentiate hard exudates from regular tissue allowing them to be segmented. After creating the model 26 images were used for testing. Results of the U-net model showed a Dice similarity coefficient of 67.23 ± 13.60%, a specificity of 99.74 ± 0.25%, and precision of 75.87 ± 18.14% when comparing the algorithm generated images to the manually segmented ground truths. These results show that the model is precisely delineating the hard exudates and therefore is a viable way to diagnose the severity of DR.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".