RA2M-UNet: Efficient medical image segmentation via reparameterized convolution, dual-domain attention and 2D state–space modeling

Deep learning has made remarkable progress across various domains, particularly in medical image segmentation. However, a persistent challenge remains in balancing accuracy and computational efficiency, as current state-of-the-art models often sacrifice one aspect to enhance the other. Here, we propose RA2M-UNet, a novel network that addresses this trade-off through key innovations: (1) a feature fusion module that integrates multi-scale dilated convolutions with 2D selective scan module (2D-SSM); (2) an enhanced 2D-SSM for better spatial and semantic dependency capture; (3) parameter-efficient structural re-parameterization; (4) multi-output supervision for further refined segmentation. Comprehensive experiments demonstrate that our approach outperforms existing methods while maintaining parameter efficiency, effectively resolving the accuracy-efficiency dilemma in medical image segmentation.