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Beyond Ground Truth: A Heuristic Unsupervised Quality Assurance Engine for Diagnosing Calibrational Blindness in Dental Segmentation — arXiv:2610.40587

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.40587
License (as archived): CC0-1.0
Archived: 2026-11-01T02:43:44.023Z
SHA-256 of the archived text: 570d938d2cbedc3c0343ce3e863726ebc3c6e903640eb67b93b04f9de8afcefb
Stable URL of this page: https://metraipolis.com/documento/dc_dc8ea2c328fc

How to cite

Beyond Ground Truth: A Heuristic Unsupervised Quality Assurance Engine for Diagnosing Calibrational Blindness in Dental Segmentation — arXiv:2610.40587 — arXiv (en), https://arxiv.org/abs/2610.40587 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dc8ea2c328fc (Metraipolis archive dc_dc8ea2c328fc, sha256 570d938d2cbedc3c0343ce3e863726ebc3c6e903640eb67b93b04f9de8afcefb)

BibTeX

@misc{dcdc8ea2c328fc,
  title = {Beyond Ground Truth: A Heuristic Unsupervised Quality Assurance Engine for Diagnosing Calibrational Blindness in Dental Segmentation — arXiv:2610.40587},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.40587},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dc8ea2c328fc; sha256 570d938d2cbedc3c0343ce3e863726ebc3c6e903640eb67b93b04f9de8afcefb},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.40587. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

In real-world clinical settings, dental X-ray segmentation using deep learning methods suffers from reliability issues. Even when the segmentation quality is bad, deep learning models show high confidence in their predictions (calibrational blindness). Moreover, in real use, doctors do not have ground-truth masks to check their accuracy. To address this problem, we introduce a simple quality-checking engine called HUS-QA with three stages. We train it on the PRAD (Periapical Dental X-ray) dataset to build our SimCLR self-supervised ResNet-18 U-Net model, which generates the segmentation mask and then evaluates its quality. Our HUS-QA checks the segmentation mask quality without a ground-truth mask. Prediction confidence, edge sharpness, shape connectivity, and area-size penalty are the simple indicators considered in this research. Results show that standard confidence methods fail (r = -0.1394, p = 1.7864e-3, ROC AUC = 0.4275). By contrast, our HUS-QA shows a positive correlation with actual Dice scores (r = 0.4647, p = 3.7510e-28) and achieves a strong ROC-AUC of 0.8405. Therefore, HUS-QA serves as a practical tool for clinical quality control.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2610.40587 — 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 2026-11-01T02:16:43.994Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).