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...
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| Published in: | Proceedings / IEEE Workshop on Applications of Computer Vision pp. 1893 - 1903 |
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| Main Authors: | , , , , |
| Format: | Conference Proceeding |
| Language: | English |
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26.02.2025
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| ISSN: | 2642-9381 |
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| Abstract | 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 seman-tic 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 Ag-gregator 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 factor-ized 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 are available at: https://github.com/kabbas570/CAMS-Net. |
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| AbstractList | 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 seman-tic 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 Ag-gregator 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 factor-ized 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 are available at: https://github.com/kabbas570/CAMS-Net. |
| Author | Khan, Abbas Asad, Muhammad Benning, Martin Slabaugh, Gregory Roney, Caroline |
| Author_xml | – sequence: 1 givenname: Abbas surname: Khan fullname: Khan, Abbas organization: Queen Mary University of London – sequence: 2 givenname: Muhammad surname: Asad fullname: Asad, Muhammad organization: Queen Mary University of London – sequence: 3 givenname: Martin surname: Benning fullname: Benning, Martin organization: University College,London – sequence: 4 givenname: Caroline surname: Roney fullname: Roney, Caroline organization: Queen Mary University of London – sequence: 5 givenname: Gregory surname: Slabaugh fullname: Slabaugh, Gregory organization: Queen Mary University of London |
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| Snippet | Convolutional Neural Networks (CNNs) and Transformer-based self-attention models have become the standard for medical image segmentation. This paper... |
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| StartPage | 1893 |
| SubjectTerms | cardiac imaging Computational modeling Computer vision Convolution convolution-attention-free Convolutional neural networks Data mining Feature extraction Image segmentation mamba based segmentation semantic segmentation Source coding Transformers |
| Title | CAMS: Convolution and Attention-Free Mamba-Based Cardiac Image Segmentation |
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