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AFENet: Attention-driven feature evolution network for precise polyp segmentation
DOI:10.1016/j.bspc.2026.111306.png)
Abstract
En 中文
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Dual-stage feature evolution dynamically refines representations for polyp segmentation.
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CaRL suppresses irrelevant activations and enhances discriminative feature learning.
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AUR refines uncertainty estimation and reduces false negatives and positives.
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Achieves superior performance on PolypGen, PSNBI2K, and PICCOLO datasets.
Journal
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