MAAN: multi-scale atrous attention network for skin lesion segmentation

Abstract

Abstract Skin cancer research is essential to finding new treatments and improving survival rates in computer-aided medicine. Within this research, the accurate segmentation of skin lesion images is an important step for both early diagnosis and personalized treatment strategies. However, while current popular Transformer-based models have achieved competitive segmentation results, they often ignore the computational complexity and the high costs associated with their training. In this paper, we propose a lightweight network, a multi-scale atrous attention network for skin lesion segmentation (MAAN). Firstly, we optimize the residual basic block by constructing a dual-path framework with both high and low-resolution paths, which reduces the number of parameters while maintaining effective feature extraction capability. Secondly, to better capture the information in the skin lesion images and further improve the model performance, we design an adaptive multi-scale atrous attention module at the final stage of the low-resolution path. The experiments conducted on the ISIC 2017 and ISIC2018 datasets show that the proposed model MAAN achieves mIoU of 85.20 and 85.67% respectively, outperforming recent MHorNet while maintaining only 0.37M parameters and 0.23G FLOPs computational complexity. Additionally, through ablation studies, we demonstrate that the AMAA module can work as a plug-and-play module for performance improvement on CNN-based methods.

Affiliated Institutions

• University of Jinan CN
• Qufu Normal University CN
• Shandong Women’s University CN

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