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[bibtex]@InProceedings{Khan_2025_WACV, author = {Khan, Abbas and Asad, Muhammad and Benning, Martin and Roney, Caroline and Slabaugh, Gregory}, title = {CAMS: Convolution and Attention-Free Mamba-Based Cardiac Image Segmentation}, booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)}, month = {February}, year = {2025}, pages = {1893-1903} }
CAMS: Convolution and Attention-Free Mamba-Based Cardiac Image Segmentation
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 semantic Segmentation Network named CAMS-Net. Specifically we design a 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 are available at: https://github.com/kabbas570/CAMS-Net.
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