CAMS: Convolution and Attention-Free Mamba-based Cardiac Image Segmentation

• URL: http://arxiv.org/abs/2406.05786v3
• Date: Tue, 29 Oct 2024 11:29:17 GMT
• Title: CAMS: Convolution and Attention-Free Mamba-based Cardiac Image Segmentation
• Authors: Abbas Khan, Muhammad Asad, Martin Benning, Caroline Roney, Gregory Slabaugh,
• Abstract summary: Convolutional Neural Networks (CNNs) and Transformer-based self-attention models have become the standard for medical image segmentation. We present a Convolution and self-attention-free Mamba-based semantic Network named CAMS-Net. 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.
• Score: 0.508267104652645
• License:
• 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 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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