Reproduce “Learning with Geometric Priors in U-Net Variants for Polyp Segmentation” on OpenResearch

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About this paper

Fabian Vazquez, Jose A. Nuñez, Diego Adame, Alissen Moreno, Augustin Zhan, Huimin Li, Jinghao Yang, Haoteng Tang and 2 more

A Geometric Prior-guided Module (GPM) is introduced to enhance real-time polyp segmentation in colonoscopy by incorporating domain-adapted pseudo-depth maps. This module consistently improves accuracy and boundary sharpness across U-Net, U-Net v2, and VM-UNetV2 architectures, with negligible computational overhead.

Key insights

  • Explicit, domain-adapted geometric priors (depth maps) are highly effective in enhancing the accuracy and robustness of polyp segmentation, particularly in visually challenging endoscopic scenes.
  • A bidirectional information flow mechanism within the GPM, allowing interplay between depth features and segmentation features, is crucial for enforcing 3D geometric consistency and refining boundary delineation.
  • The GPM offers a lightweight and generalizable solution, demonstrating consistent performance gains across diverse U-Net architectural variants (CNN, Transformer, Mamba) with minimal added computational burden.

Summary by alphaXiv.

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