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BGD-SF PolySegNet: boundary-guided dynamic selective fusion network for robust polyp segmentation in colonoscopy images

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The paper presents BGD‑SF PolySegNet, a boundary‑guided encoder‑decoder network designed to improve polyp segmentation in colonoscopy images. It introduces three novel modules—BGD‑CMSCA, BG‑SAGF, and MBGH—that explicitly use boundary information to guide dynamic context aggregation, selective feature fusion, and mask refinement. Evaluated on multiple datasets, the model achieves state‑of‑the‑art Dice and boundary metrics while running at 35 FPS with 22.51 M parameters.

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arXiv Machine Learning
Sep 14

Beyond Accuracy: Uncertainty-Guided Boundary Refinement for Reliable Biomedical Image Segmentation

The paper introduces RABR-Net, a two‑stage framework that refines biomedical image segmentation boundaries by combining multiple uncertainty measures into a boundary‑aware representation. A gated residual refiner uses this representation to selectively correct uncertain boundary pixels while preserving confident regions, leading to modest but statistically significant improvements in Dice, Boundary Dice, and HD95 metrics on a held‑out test set. Qualitative results show the refiner focuses on uncertain cytoplasm and nucleus boundaries, though calibration does not automatically improve.

By Anima Kujur