LB-UNet: A Lightweight Boundary-Assisted UNet for Skin Lesion Segmentation

Skin lesion segmentation is vital in computer-aided diagnosis and treatment of skin diseases. UNet and its variants have been widely utilized for skin lesion segmentation. However, resource constraints limit the deployment of larger parameter models on edge devices. To address this issue, we propose a novel lightweight boundary-assisted UNet (LB-UNet) for skin lesion segmentation. LB-UNet incorporates the Group Shuffle Attention module (GSA) to significantly reduce the model’s parameters and computational demands. Furthermore, to enhance the model’s segmentation capability, especially in handling ambiguous boundary, LB-UNet introduces the Prediction Map Auxiliary module (PMA). Briefly, PMA consists of three modules: (1) Segmentation Region and Boundary Prediction module is utilized to predict the segmentation region and boundary of the decoder features; (2) GA-Based Boundary Generator is employed to generate the ground truth boundary map through genetic algorithm; (3) Prediction Information Fusion module enhances the skip connection by leveraging the prediction information. By combining this modules, the region and boundary information is effectively integrated into the backbone. The experiment results on the ISIC2017 and ISIC2018 datasets demonstrate that LB-UNet outperforms current lightweight methods. To the best of our knowledge, LB-UNet the first model with a parameters count limited to 38KB and Giga-Operations Per Second (GFLOPs) limited to 0.1. The codes and trained models are publicly available at https://github.com/xuxuxuxuxuxjh/LB-UNet .