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.