ASP-VMUNet: Atrous Shifted Parallel Vision Mamba U-Net for Skin Lesion Segmentation
Journal:
arXiv
Published Date:
Mar 25, 2025
Abstract
Skin lesion segmentation is a critical challenge in computer vision, and it
is essential to separate pathological features from healthy skin for
diagnostics accurately. Traditional Convolutional Neural Networks (CNNs) are
limited by narrow receptive fields, and Transformers face significant
computational burdens. This paper presents a novel skin lesion segmentation
framework, the Atrous Shifted Parallel Vision Mamba UNet (ASP-VMUNet), which
integrates the efficient and scalable Mamba architecture to overcome
limitations in traditional CNNs and computationally demanding Transformers. The
framework introduces an atrous scan technique that minimizes background
interference and expands the receptive field, enhancing Mamba's scanning
capabilities. Additionally, the inclusion of a Parallel Vision Mamba (PVM)
layer and a shift round operation optimizes feature segmentation and fosters
rich inter-segment information exchange. A supplementary CNN branch with a
Selective-Kernel (SK) Block further refines the segmentation by blending local
and global contextual information. Tested on four benchmark datasets
(ISIC16/17/18 and PH2), ASP-VMUNet demonstrates superior performance in skin
lesion segmentation, validated by comprehensive ablation studies. This approach
not only advances medical image segmentation but also highlights the benefits
of hybrid architectures in medical imaging technology. Our code is available at
https://github.com/BaoBao0926/ASP-VMUNet/tree/main.
