J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation
This work addresses segmentation accuracy for complex anatomical structures in medical imaging, which is crucial for diagnosis and treatment planning, but it appears incremental as it builds on existing transformer and attention methods.
The paper tackled the problem of medical image segmentation by proposing a transformer-based architecture with joint channel and pyramid attention, achieving a 6.9% improvement in Mean Dice score and a 39.9% improvement in Hausdorff Distance over a baseline on the Synapse dataset.
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. Increasing model complexity requires more training data, and we further improve model generalization with CutMix data augmentation. Our approach is evaluated on the Synapse multi-organ segmentation dataset, achieving a 6.9% improvement in Mean Dice score and a 39.9% improvement in Hausdorff Distance (HD95) over an implementation without our enhancements. Our proposed model demonstrates improved segmentation accuracy for complex anatomical structures, outperforming existing state-of-the-art methods.