DepthPolyp: Pseudo-Depth Guided Lightweight Segmentation for Real-Time Colonoscopy
Accurate polyp segmentation in colonoscopy is essential for early colorectal cancer detection, yet real-world clinical environments pose persistent challenges such as motion blur, specular reflections, and illumination instability. Most existing methods are optimized on clean benchmark images and suffer noticeable performance degradation when deployed in authentic surgical scenarios. We propose DepthPolyp, a lightweight and robust segmentation framework based on pseudo-depth-guided multi-task learning and efficient feature modulation. The architecture combines hierarchical Ghost factorization for compact feature generation, Interleaved Shuffle Fusion for low-cost cross-scale interaction, ...
| Authors | Zhuoyu Wu, Wenhui Ou, Lexi Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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