CAMS: Convolution and Attention-Free Mamba-based Cardiac Image Segmentation
<|reference_start|>CAMS: Convolution and Attention-Free Mamba-based Cardiac Image Segmentation: Convolutional Neural Networks (CNNs) and Transformer-based self-attention models have become the standard for medical image segmentation. This paper demonstrates that convolution and self-attention, while widely used, are not the only effective methods for segmentation. Breaking with convention, we present a Convolution and self-Attention-free Mamba-based semantic Segmentation Network named CAMS-Net. Specifically, we design Mamba-based Channel Aggregator and Spatial Aggregator, which are applied independently in each encoder-decoder stage. The Channel Aggregator extracts information across different channels, and the Spatial Aggregator learns features across different spatial locations. We also propose a Linearly Interconnected Factorized Mamba (LIFM) block to reduce the computational complexity of a Mamba block and to enhance its decision function by introducing a non-linearity between two factorized Mamba blocks. Our model outperforms the existing state-of-the-art CNN, self-attention, and Mamba-based methods on CMR and M&Ms-2 Cardiac segmentation datasets, showing how this innovative, convolution, and self-attention-free method can inspire further research beyond CNN and Transformer paradigms, achieving linear complexity and reducing the number of parameters. Source code and pre-trained models will be publicly available upon acceptance.<|reference_end|>
@article{khan2024cams:,
    title={CAMS: Convolution and Attention-Free Mamba-based Cardiac Image
  Segmentation},
    author={Abbas Khan, Muhammad Asad, Martin Benning, Caroline Roney, Gregory
  Slabaugh},
    journal={arXiv preprint arXiv:2406.05786},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05786},
    primaryClass={cs.CV}
}