Abstract:

Segmentation of skin lesions is an important task in medical image analysis, and is of great significance for the early diagnosis and treatment of skin diseases. However, when processing high-resolution skin images and capturing subtle lesion features, existing models still face challenges such as high computational complexity and insufficient processing of redundant information. To address this end, a lightweight skin lesion image segmentation network based on the Mamba structure was proposed, ResMamba adopted a six-level U-shaped structure, embedding Mamba into the visual state space and introducing it into the codec. The ResVSS module, as the core component of the encoder, reduced the number of parameters by removing a redundant linear layer, and at the same time combined the deep convolution block and learnable scale parameters to scale the residual connection, thereby reducing the complexity of the model while improving the segmentation accuracy. In the hopping connection module, a multi-level multi-scale information fusion module was used to generate spatial and channel attention maps, which effectively fused multi-scale information. Through experimental verification on the public skin dataset ISIC2017 and ISIC2018, the results demonstrated that the ResMamba model achieved good segmentation performance in terms of the number of balance parameters and segmentation performance, thus verifying the effectiveness of the model.

Key words: deep learning, skin lesion segmentation, mamba structure, state space models, lightweight