Cracking the code: enhancing interpretability and accessibility of ophthalmic disorder detection using Kolmogorov-Arnold Network
Bibliographic record
Abstract
This study emphasizes the importance of interpretability and accessibility in deep learning models applied to ophthalmology, particularly in Optical Coherence Tomography (OCT) analysis. The “black box” nature of many deep learning models presents challenges for clinicians, who require transparent explanations for model decisions to build trust and ensure accountability. Kolmogorov-Arnold Networks (KANs) offer a promising solution by decomposing complex functions into simpler, interpretable components, making them more suitable for clinical settings. This study explores the application of KANs in OCT analysis, focusing on feature decomposition, rule extraction, and visualization to enhance interpretability. Preliminary results indicate that for models combining Convolutional Neural Networks (CNNs) with KANs, particularly those with smaller parameter counts, performance is comparable to larger, more complex models while remaining more accessible and interpretable. For instance, a CNN+KAN model with 0.3M parameters achieved a significant performance boost from 60% to 88.79% accuracy after pre-training on the MNIST dataset, using only 1.4% of the parameters of the best-performing ResNet-34 model. This highlights the potential of KANs in reducing model complexity without sacrificing performance, making them suitable for deployment in resource-limited environments. The study calls for further investigation into the balance between model complexity and interpretability, aiming to improve diagnostic methodologies and build trust in AI systems in ophthalmology.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".