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<p>Accurate dermoscopic lesion segmentation is challenging because existing methods struggle to preserve fine-grained local structures while capturing long-range semantic context, leading to reduced robustness against unclear boundaries, imaging artifacts, and dataset shifts. We propose Hyper-Fusion Segmentation (H-Fusion SEG), a dual-branch framework that combines a boundary-sensitive U-Net encoder–decoder with a Segment Anything Model branch to jointly extract high-resolution local details and robust global semantics. A novel hyper-attention fusion module adaptively integrates these heterogeneous features and is optimized with boundary-aware objectives to enhance delineation and interpretability. On the ISIC-2016 dataset, H-Fusion SEG achieves IoU = 0.8775 and Dice = 0.9269 (+ 1.28% IoU, + 1.38% Dice over baselines), and on ISIC-2018, it achieves IoU = 0.9329 and Dice = 0.9629 (+ 8.69% IoU, + 6.69% Dice over baselines), with strong generalization to the HAM10000 dataset. These gains are particularly pronounced for complex lesions with indistinct or ambiguous boundaries. The proposed framework offers a flexible and generalizable solution for medical image segmentation, with promising potential for precise and reliable computer-aided diagnostic tools in dermatology. Code is available at: <a href="https://github.com/AnasHXH/Skin-DiseaseS-Segmentation">https://github.com/AnasHXH/Skin-DiseaseS-Segmentation</a>.</p>

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H-fusion SEG: dual-branch hyper-attention fusion network with SAM integration for robust skin disease segmentation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月29日

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  • Walid El‑Shafai,
  • Anas M. Ali,
  • Nada Alzaben,
  • Ibrahim Abd El‑Fattah

摘要

Accurate dermoscopic lesion segmentation is challenging because existing methods struggle to preserve fine-grained local structures while capturing long-range semantic context, leading to reduced robustness against unclear boundaries, imaging artifacts, and dataset shifts. We propose Hyper-Fusion Segmentation (H-Fusion SEG), a dual-branch framework that combines a boundary-sensitive U-Net encoder–decoder with a Segment Anything Model branch to jointly extract high-resolution local details and robust global semantics. A novel hyper-attention fusion module adaptively integrates these heterogeneous features and is optimized with boundary-aware objectives to enhance delineation and interpretability. On the ISIC-2016 dataset, H-Fusion SEG achieves IoU = 0.8775 and Dice = 0.9269 (+ 1.28% IoU, + 1.38% Dice over baselines), and on ISIC-2018, it achieves IoU = 0.9329 and Dice = 0.9629 (+ 8.69% IoU, + 6.69% Dice over baselines), with strong generalization to the HAM10000 dataset. These gains are particularly pronounced for complex lesions with indistinct or ambiguous boundaries. The proposed framework offers a flexible and generalizable solution for medical image segmentation, with promising potential for precise and reliable computer-aided diagnostic tools in dermatology. Code is available at: https://github.com/AnasHXH/Skin-DiseaseS-Segmentation.