Graph-based representation of retinal lesions for an interpretable diagnosis of diabetic retinopathy
Bibliographic record
Abstract
Diabetic retinopathy (DR) is a leading cause of blindness among the working-age population worldwide. DR diagnosis and grading are based on identifying characteristic retinal lesions through fundus imaging. Despite the effectiveness of deep learning techniques in DR detection and grading, these methods often lack interpretability. This work introduces a novel approach that leverages a graph representation of the retina, where each node corresponds to a lesion, and a graph neural network (GNN) is used to grade DR. Our method aligns with clinical guidelines by using lesion-specific information while maintaining the capacity of deep learning models. We first segment DR lesions using a pre-trained convolutional neural network (CNN) and then construct a graph with lesions as nodes connected to their k nearest neighbours. Features for each node are derived from the lesion-specific regions in the fundus image. The resulting lesion graph is classified using a graph attention network (GAT) to make the DR grade prediction. Our method was evaluated on multiple public datasets, achieving performance comparable to state-of-the-art techniques based on quadratic weighted Cohen’s kappa and other metrics. This graph-based approach offers a balance between local lesion segmentation and global image classification, potentially enhancing interpretability and robustness for clinical applications.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.002 | 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".