SCDL: Synergistic Confidence-Dispersion Learning for Semi-Supervised Video Polyp Segmentation

Theearlydetectionandpreventionofcolorectalcancer(CRC)ishighly dependentontheaccuratepolypsegmentationfromendoscopicvideos.However, existing video polyp segmentation (VPS) methods are largely hampered by the scarcity of large-scale, high-quality pixel-level annotations. While some semisupervised VPS models have been proposed, their performance is limited by the unreliability of pseudo-labels produced in the training process. In addition, inherent challenges of endoscopic videos, such as low contrast, significant inter-frame variations, and consecutive low-quality frames, further exacerbate the above issues. To address these shortcomings, we propose a unified semi-supervised video polyp segmentation framework, dubbed synergistic confidence–dispersion learning (SCDL), which aims to simultaneously improve pseudo-label reliability, reduce regional supervision bias, and alleviate ambiguities caused by low-contrast features. Our framework has three innovative components: (1) a new spectral convex optimization separation (SCOS) algorithm, which employs convex optimization in a confidence–dispersion space for robust pseudo-label selection, (2) contextual consistency perturbation (CCP), a novel augmentation strategy that reduces regional supervision bias by forcing semantic reconstruction from unreliable areas; and (3) a prototype-guided adaptive refinement (PAR) module that leveragesprototype-drivenaggregationandobject-awarefusiontosharpenfeature discrimination, suppress background clutter, and strengthen temporal coherence. Extensive experiments demonstrate that our model consistently achieves SOTA performance on the SUN-SEG dataset across all labeled-data ratios, and exhibits zero-shot generalization capability on the unseen CVC-ClinicDB dataset. Code are available at https://github.com/Yuanqin-He/SCDL.