Metraipolis › archive › dc_8a36becf2ffc

CasCVS-Net: A Staged Multi-Task Cascade for Critical View of Safety Assessment — arXiv:2609.27681

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.27681
License (as archived): CC0-1.0
Archived: 2026-10-08T12:34:58.755Z
SHA-256 of the archived text: c946b73bea6dd4ecda201ea63a0578eae499a07e110608643bb653d5f32c942d
Stable URL of this page: https://metraipolis.com/documento/dc_8a36becf2ffc

How to cite

CasCVS-Net: A Staged Multi-Task Cascade for Critical View of Safety Assessment — arXiv:2609.27681 — arXiv (en), https://arxiv.org/abs/2609.27681 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_8a36becf2ffc (Metraipolis archive dc_8a36becf2ffc, sha256 c946b73bea6dd4ecda201ea63a0578eae499a07e110608643bb653d5f32c942d)

BibTeX

@misc{dc8a36becf2ffc,
  title = {CasCVS-Net: A Staged Multi-Task Cascade for Critical View of Safety Assessment — arXiv:2609.27681},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.27681},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_8a36becf2ffc; sha256 c946b73bea6dd4ecda201ea63a0578eae499a07e110608643bb653d5f32c942d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.27681. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Automated assessment of the Critical View of Safety (CVS) in laparoscopic cholecystectomy requires both recognition of the three CVS criteria and anatomical grounding in small, rare, and often occluded hepatocystic structures. Learning-based methods differ in the anatomical information they use, from image-level classification to detection, segmentation, or graph-based reasoning, yet grounding the safety-critical anatomy remains the main bottleneck. We propose CasCVS-Net, a staged multi-task cascade that jointly performs object detection, semantic segmentation, and CVS assessment, trained on the Endoscapes dataset. The model couples the tasks through predicted anatomy: predicted boxes guide segmentation, and predicted masks provide region-level features for CVS classification, so CVS assessment at inference uses only model predictions rather than ground-truth annotations. To reduce optimisation instability in this coupled setting, training progresses from detection to detection-segmentation and then to the full three-task cascade, followed by task-wise fine-tuning. Evaluation on the public unseen test set shows that CasCVS-Net improves over matched single-task baselines on all three tasks, achieving 32.0 detection mAP, 46.8 semantic mIoU, 15.3 rare-anatomy mIoU, and 67.2 CVS mAP. It outperforms the state-of-the-art LG-CVS and SV2LSTG by 6.3% and 4.5% relative CVS mAP, respectively, corresponding to 4.0 and 2.9 mAP points. These results show that staged task coupling through predicted boxes and masks improves anatomical grounding for CVS assessment, particularly for rare hepatocystic structures.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-24 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.27681 — 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 2026-10-08T12:12:35.709Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-24?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).