{"ID":2827713,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.16977","arxiv_id":"2512.16977","title":"Endo-SemiS: Towards Robust Semi-Supervised Image Segmentation for Endoscopic Video","abstract":"In this paper, we present Endo-SemiS, a semi-supervised segmentation framework for providing reliable segmentation of endoscopic video frames with limited annotation. EndoSemiS uses 4 strategies to improve performance by effectively utilizing all available data, particularly unlabeled data: (1) Cross-supervision between two individual networks that supervise each other; (2) Uncertainty-guided pseudo-labels from unlabeled data, which are generated by selecting high-confidence regions to improve their quality; (3) Joint pseudolabel supervision, which aggregates reliable pixels from the pseudo-labels of both networks to provide accurate supervision for unlabeled data; and (4) Mutual learning, where both networks learn from each other at the feature and image levels, reducing variance and guiding them toward a consistent solution. Additionally, a separate corrective network that utilizes spatiotemporal information from endoscopy video to improve segmentation performance. Endo-SemiS is evaluated on two clinical applications: kidney stone laser lithotomy from ureteroscopy and polyp screening from colonoscopy. Compared to state-of-the-art segmentation methods, Endo-SemiS substantially achieves superior results on both datasets with limited labeled data. The code is publicly available at https://github.com/MedICL-VU/Endo-SemiS","short_abstract":"In this paper, we present Endo-SemiS, a semi-supervised segmentation framework for providing reliable segmentation of endoscopic video frames with limited annotation. EndoSemiS uses 4 strategies to improve performance by effectively utilizing all available data, particularly unlabeled data: (1) Cross-supervision betwee...","url_abs":"https://arxiv.org/abs/2512.16977","url_pdf":"https://arxiv.org/pdf/2512.16977v1","authors":"[\"Hao Li\",\"Daiwei Lu\",\"Xing Yao\",\"Nicholas Kavoussi\",\"Ipek Oguz\"]","published":"2025-12-18T18:58:01Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":605825,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2827713,"paper_url":"https://arxiv.org/abs/2512.16977","paper_title":"Endo-SemiS: Towards Robust Semi-Supervised Image Segmentation for Endoscopic Video","repo_url":"https://github.com/MedICL-VU/Endo-SemiS","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}