UltraLight Vision Mamba UNet: A Highly Efficient Model for Skin Lesion Segmentation with Significantly Reduced Parameters
The proposed UltraLight Vision Mamba UNet (UltraLight VM-UNet) is a highly efficient model for skin lesion segmentation, achieving excellent performance with only 0.049M parameters and 0.060 GFLOPs, which is significantly lower than existing lightweight Vision Mamba models.
AC-MambaSeg: An Adaptive Convolution and Mamba-Based Architecture for Enhanced Skin Lesion Segmentation
The proposed AC-MambaSeg model combines the strengths of CNN and Vision Mamba to effectively capture both local and global features for accurate skin lesion segmentation, further enhanced by advanced components like CBAM and Selective Kernels.
Robust Skin Lesion Segmentation Using an Attention-based Dilated Convolutional Residual Network with Guided Decoder
The proposed AD-Net utilizes a dilated convolutional residual network with an attention-based spatial feature enhancement block and a guided decoder strategy to achieve robust and efficient skin lesion segmentation.
SkinMamba: An Efficient Hybrid Architecture for Precise Skin Lesion Segmentation with Cross-Scale Global Modeling and Frequency-Guided Boundary Detection
SkinMamba, a hybrid architecture combining the strengths of Mamba and CNN, effectively addresses challenges in skin lesion segmentation such as varying lesion sizes and unclear boundaries through cross-scale global modeling and frequency-guided boundary detection.
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