Year: 2025
Type: conference-paper
Abstract: 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.
Institution: University of California, Santa Cruz
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4413278351