TRIAGE: Risk-Controlled Pseudo-Label Admission for Annotation-Efficient Semi-Supervised Retinal OCT Classification

2026-08-14

AI Summary

This study addresses the high annotation costs and asymmetric error risks in semi-supervised retinal OCT classification by proposing a risk-controllable semi-supervised framework. The method integrates a hierarchical classifier, a context-aware Transformer teacher model, and a patient-level conformal risk controller, while incorporating an asymmetric cost matrix to mitigate under-grading. Experimental results demonstrate that with only 20% labeled data, the model achieves 89.66% accuracy and reduces the under-grading rate by 42.7% compared to state-of-the-art methods. This work effectively resolves clinical safety concerns in low-resource settings, significantly enhancing the reliability of computer-aided diagnosis systems for retinal diseases.

Abstract

The advanced retinal disease diagnosing imaging modality, optical coherence tomography (OCT), encounters a lack of automation because of the high expenses for annotations performed by specialists. The use of SSL solves the problem of insufficient annotations using unlabeled B-scans; however, most of the current techniques for generating pseudo-labels are based on prediction confidence without considering the asymmetry between different types of errors. This paper proposes TRIAGE, a risk-controlled semi-supervised framework for OCT scans classification, which uses the concept of a patient-level conformal risk controller with an asymmetric cost matrix. TRIAGE unites three crucial modules: a hierarchical classifier that is capable of working with partially abnormal supervision of the disease subtypes, a patient-grouped conformal risk controller with primal-dual coverage control, and a context-aware Transformer teacher for cross-slice verification. On the dataset from Noor Eye Hospital (16,822 B-scans, 161 patients, and 554 volumes) with a test set of unseen patients, TRIAGE demonstrates 89.66% scan-level accuracy, 0.8805 macro-F1, 0.9641 macro-AUC, and an 8.34% under-grading rate when using only 20% of the labeled data. With only 5% of the labeled data, TRIAGE keeps 76.88% accuracy and a 0.1656 under-grading rate. Compared with the other six state-of-the-art semi-supervised methods, TRIAGE significantly outperforms them with ablation study demonstrating the contribution of each module in the overall framework performance (by 42.7% in terms of under-grading rate comparing to fixed threshold methods). TRIAGE demonstrates 98.00% accuracy for 3-class classification with 1% labeled data and 95.94% accuracy for 8-class classification with 10% labeled data on the OCT-C8 dataset.

Problem

• Semi-supervised learning
• Retinal OCT classification
• Pseudo-labeling
• Asymmetric error cost
• Risk control

Innovation

• Risk-Controlled Semi-Supervised Learning
• Conformal Risk Controller
• Asymmetric Cost Matrix
• Context-Aware Transformer
• Retinal OCT Classification