GCA-ResUNet for Medical Image Segmentation
Paper#Medical Image Segmentation🔬 Research|Analyzed: Jan 3, 2026 15:57•
Published: Dec 30, 2025 05:13
•1 min read
•ArXivAnalysis
This paper introduces GCA-ResUNet, a novel medical image segmentation framework. It addresses the limitations of existing U-Net and Transformer-based methods by incorporating a lightweight Grouped Coordinate Attention (GCA) module. The GCA module enhances global representation and spatial dependency capture while maintaining computational efficiency, making it suitable for resource-constrained clinical environments. The paper's significance lies in its potential to improve segmentation accuracy, especially for small structures with complex boundaries, while offering a practical solution for clinical deployment.
Key Takeaways
- •Proposes GCA-ResUNet, a new medical image segmentation framework.
- •Employs a Grouped Coordinate Attention (GCA) module for improved performance.
- •Outperforms existing CNN and Transformer-based methods on benchmark datasets.
- •Offers a favorable trade-off between accuracy and computational efficiency.
- •Suitable for resource-constrained clinical environments.
Reference / Citation
View Original"GCA-ResUNet achieves Dice scores of 86.11% and 92.64% on Synapse and ACDC benchmarks, respectively, outperforming a range of representative CNN and Transformer-based methods."