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TRIAGE: Risk-Controlled Pseudo-Label Admission for Annotation-Efficient Semi-Supervised Retinal OCT Classification — arXiv:2608.14321

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.14321
License (as archived): CC0-1.0
Archived: 2026-10-19T04:04:30.313Z
SHA-256 of the archived text: bea3b0df4a67eb41b153feb1425ff3f81c0306660e43ea627f107cecde29f339
Stable URL of this page: https://metraipolis.com/documento/dc_031dfe2cd25b

How to cite

TRIAGE: Risk-Controlled Pseudo-Label Admission for Annotation-Efficient Semi-Supervised Retinal OCT Classification — arXiv:2608.14321 — arXiv (en), https://arxiv.org/abs/2608.14321 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_031dfe2cd25b (Metraipolis archive dc_031dfe2cd25b, sha256 bea3b0df4a67eb41b153feb1425ff3f81c0306660e43ea627f107cecde29f339)

BibTeX

@misc{dc031dfe2cd25b,
  title = {TRIAGE: Risk-Controlled Pseudo-Label Admission for Annotation-Efficient Semi-Supervised Retinal OCT Classification — arXiv:2608.14321},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.14321},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_031dfe2cd25b; sha256 bea3b0df4a67eb41b153feb1425ff3f81c0306660e43ea627f107cecde29f339},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.14321. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-17 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.14321 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T03:57:57.954Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).