J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation

Medical image segmentation is crucial for diagnosis and treatment planning. Traditional CNN-based models, like U-Net, have shown promising results but struggle to capture long-range dependencies and global context. To address these limitations, we propose a transformer based architecture that jointly applies Channel Attention and Pyramid Attention mechanisms to improve multi-scale feature extraction and enhance segmentation performance for medical images. We evaluate our approach on the Synapse multi-organ segmentation dataset and report significant improvements in Mean Dice score and Hausdorff Distance (HD95) compared to TransUNet, a widely adopted baseline. Our model also outperforms other state-of-the-art methods, demonstrating improved segmentation accuracy for complex anatomical structures.