Using SHAP Analysis to Detect Areas Contributing to Diabetic Retinopathy Detection
Bibliographic record
Abstract
Diabetic retinopathy (DR) is known as an important cause of blindness worldwide and serious public health concern in the population aged 20–65. With the burgeoning number of diabetes globally and its effects on patients' vision, the automatic detection of DR has received wide attention from the machine learning field. However, due to the black-box nature of deep learning and machine learning models, the interpretation and reliability of the predictions is still an issue that needs to be addressed for the successful deployment of these predictive models. In this paper, we use the SHapley Additive exPlanations (SHAP) analysis approach to detect areas of an eye image that contribute the most to the prediction of DR using transfer learning. Our predictive model achieves an accuracy of 97% and 81% for binary and multi-class classification of DR. Our SHAP analysis results show that regardless of the performance of the model, this approach can be used as a tool to interpret the prediction results with more context-sensitive information about each sample, and better understand the reasons for the classification results.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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 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".