Enhanced Macular Telangiectasia Type 2 Detection: Leveraging Self-Supervised Learning and Ensemble Models
Bibliographic record
Abstract
Objective: To investigate an ensemble-based approach utilizing deep learning models for accurate and interpretable detection of macular telangiectasia (MacTel) type 2 on OCT imaging. Design: Retrospective analysis of OCT scans, model development, and assessment. Participants: A total of 5200 OCT images from participants in the MacTel Registry conducted by the Lowy Medical Research Institute and from the University of Washington (780 MacTel patients and 1900 non-MacTel patients). Methods Intervention or Testing: We trained multiple individual MacTel vs. non-MacTel classification models using traditional supervised learning and self-supervised learning (SSL) and ensembled them using average weighting methods. We investigated diverse methodologies for constructing the ensemble, including varied architectural configurations and learning paradigms of individual models, and manipulating the amount of labeled data accessible for training. Model performance was compared against human expert graders on held-out test set data. Model interpretability was investigated using gradient-weighted class activation maps (Grad-CAM) visualization and by evaluating interrater agreement. Main Outcome Measures: For model performance, area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), accuracy, sensitivity, and specificity were reported. For interpretability, interrater agreements and Grad-CAM visualization results were evaluated. Results: Despite access to only 419 OCT volumes, including 185 MacTel patients within the 10% labeled training dataset, the ensemble model demonstrated a performance level (AUROC 0.972 [95% confidence interval (CI), 0.971-0.973], AUPRC 0.967 [95% CI, 0.965-0.969], accuracy 91.7%, sensitivity 0.905, and specificity 0.925) comparable to the human experts ensemble (AUROC 0.977 [95% CI, 0.975-0.978], AUPRC 0.987 [95% CI, 0.986-0.987], accuracy 96.8%, sensitivity 0.929, and specificity 1) on a test set of 500 patients. The individual models did not achieve the same performance levels when evaluated separately. Conclusions: Even with limited data, combining SSL with ensemble approaches improved MacTel classification accuracy and interpretation compared to the individual models. Self-supervised learning captures meaningful representations from unlabeled data, a key benefit in the setting of limited data such as with rare diseases. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".