BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation

Accurate medical image segmentation is vital for clinical quantification, disease diagnosis, treatment planning, and other applications. Convolution-based U-shaped architectures excel at learning local features but rely heavily on image-specific inductive biases inherent to convolutions. Transformer-based models, on the other hand, effectively capture long-range dependencies using self-attention but face challenges of quadratic computational and memory demands as sequence lengths increase. To address these limitations, we propose BRAU-Net++, a hybrid CNN-Transformer network that integrates the strengths of both paradigms within a U-shaped architecture. The proposed BRAUNet++ adopts the two key ideas. First, it…