BE-Unet: a boundary-enhanced UNet for skin lesion segmentation

Abstract

Automated skin lesion segmentation is critical for computer-aided diagnosis (CAD) of dermatological diseases. While UNet and its variants are widely adopted, they face challenges in extracting robust contextual features and aggregating discriminative information due to intrinsic intra-class variation, inter-class similarity, and noise interference in medical images. To address these limitations, we propose BE-Unet, a novel Boundary-Enhanced UNet architecture. BE-Unet integrates three key innovations: Large Paralleling Kernel Attention (LPKA) enhances focus on critical features prone to degradation during early encoding and late decoding stages. Dynamic Weighted Group Multi-axis Hadamard Attention (DHA) augments global-local feature perception. Boundary Enhancement Module (BEM) explicitly addresses ambiguous boundaries via dual-task learning, comprising: (1) Segmentation-Boundary Generation (SBG): Simultaneously predicts lesion masks and boundary maps. (2) Feature Enhancement Fusion (FEF): Dynamically fuses multi-scale features using segmentation and boundary guidance. Extensive experiments on ISIC2017 and ISIC2018 datasets demonstrate that BE-Unet significantly outperforms state-of-the-art methods in segmentation accuracy and boundary delineation, with a parameters count limited to 38 KB and Giga-Operations Per Second (GFLOPs) limited to 0.1.