结合通道与金字塔注意力，提升医学图像分割精度
J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation
- 设计联合通道与金字塔注意力机制，增强多尺度特征提取
- 在Synapse数据集上，平均Dice提升6.9%，HD95降低39.9%
- 适合需要高精度分割复杂器官的医学影像研究者
医学图像分割对诊断和治疗规划至关重要。传统基于CNN的模型（如U-Net）虽表现良好，但难以捕捉长距离依赖和全局上下文。为此，我们提出一种基于Transformer的架构，联合使用通道注意力与金字塔注意力机制，以提升多尺度特征提取能力并增强分割性能。为应对模型复杂度上升带来的过拟合风险，进一步采用CutMix数据增强策略提升泛化能力。在Synapse多器官分割数据集上的实验表明，相比未使用改进的方法，本模型平均Dice得分提升6.9%，Hausdorff距离（HD95）改善39.9%。所提方法在复杂解剖结构分割上表现出更高精度，优于现有先进方法。
原文摘要 · Abstract (English)
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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