BSLANet: a boundary and spatial localization-aware synergistic enhancement network for skin lesion segmentation

Abstract

Abstract. Accurate segmentation of skin lesions, a vital step in early skin cancer detection, is of utmost importance in improving patient survival rates. Although many deep learning-based methods have significantly progressed, challenges such as fuzzy lesion boundaries and complex spatial distribution remain. To resolve the above issues, this study proposes BSLANet, a novel boundary and spatial localization-aware synergistic enhancement network for skin lesion segmentation. To mitigate high-frequency information loss during downsampling, we introduce the Wavelet Attention Guided Downsampling Module (WAGDM). Furthermore, to enhance spatial understanding of complex lesions, we propose the Mixed Pooling Spatial Perception Attention (MPSPA). Lastly, to delineate finer-grained lesion boundaries and recalibrate lesion positions, we use the Differential Contrast Collaborative Feature Fusion Module (DCCFM) to improve semantic interaction in cross-layer feature fusion. Our experiments on four public skin lesion datasets—ISIC2016, ISIC2017, ISIC2018, and PH2—demonstrate that BSLANet surpasses current methods, achieving superior performance in skin lesion segmentation.