WMSM-Net: Wavelet-enhanced Mamba with spatial-channel mutual-feedback attention network for skin lesion segmentation

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Abstract

Accurate skin lesion segmentation remains challenging due to vague boundaries and heavy artifact interference in complex backgrounds. To address these issues, we introduce WMSM-Net, a frequency-space collaborative network built upon the visual state space (Mamba) architecture. WMSM-Net proposes a Wavelet-enhanced Visual State Space Block (W-VSSB) to extract high-frequency boundary details and mitigate edge loss. Concurrently, a Bidirectional Mutual-Feedback Spatial-Channel Attention Bridge (MF-SCAB) was constructed to deliver two-way guidance across spatial and channel dimensions, enhancing the perception of subtle lesions. Feature transitions were further optimized via dynamic gated fusion to suppress background noise. Extensive experiments demonstrated that WMSM-Net achieves state-of-the-art performance, yielding Dice coefficients of 91.01%, 91.08%, and 94.06% for the ISIC 2017, ISIC 2018, and PH2 datasets, respectively. The code is publicly available at https://github.com/fuxinrong2024/WMSM-Net.

Keywords

skin lesion segmentation / Mamba model / wavelet enhancement / attention mechanism / frequency-domain and spatial-domain collaboration

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