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

• URL: http://arxiv.org/abs/2411.16568v1
• Date: Mon, 25 Nov 2024 16:52:21 GMT
• Title: J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation
• Authors: Marzia Binta Nizam, Marian Zlateva, James Davis,
• Abstract summary: We propose a transformer-based architecture that jointly applies Channel Attention and Pyramid Attention mechanisms to improve multi-scale feature extraction. Our proposed model demonstrates improved segmentation accuracy for complex anatomical structures, outperforming existing state-of-the-art methods.
• Score: 2.503388496100123
• License: http://creativecommons.org/licenses/by/4.0/
• 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. 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.

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