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AFENet: Attention-driven feature evolution network for precise polyp segmentation
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DOI：10.1016/j.bspc.2026.111306.png)
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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
IF:
4.9
Papers:
1.0W
Citations:
2.4W
Organization
Cited Papers
BMANet: Boundary-guided multi-level attention network for polyp segmentation in colonoscopy images
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MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation.png)
NEURAL NETWORKS
OAAI
NEURAL NETWORKSIF6.3
OAAIShareSaveShareSaveShareSaveUM-Net: Rethinking ICGNet for polyp segmentation with uncertainty modelingMEDICAL IMAGE ANALYSIS
MEDICAL IMAGE ANALYSISIF11.8
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