用分组坐标注意力提升医学图像分割精度与效率
GCA-ResUNet:Image segmentation in medical images using grouped coordinate attention
- 在残差块中嵌入分组坐标注意力，捕捉长程依赖
- Synapse数据集上Dice达86.11%，ACDC上达92.64%
- 轻量设计适合临床部署，兼顾准确率与速度
医学图像分割支撑辅助诊断、术前规划和疾病监测。尽管基于U-Net的卷积神经网络因编码器-解码器结构和跳跃连接表现良好，但难以捕捉长程依赖；基于Transformer的方法虽能建模全局上下文，却常需大量计算和训练数据。本文提出GCA-ResUNet，将分组坐标注意力（GCA）集成到ResNet-50残差块中。GCA通过分组坐标建模，联合编码通道与空间位置的全局依赖，增强特征表示与边界分割能力，且参数与浮点运算开销极低。在Synapse数据集上取得86.11%的Dice分数，在ACDC数据集上达92.64%，超越多个先进基线模型，同时保持快速推理与良好计算效率。结果表明，GCA为卷积架构提供有效全局建模能力，实现高精度、低资源消耗的医学图像分割。
原文摘要 · Abstract (English)
Medical image segmentation underpins computer-aided diagnosis and therapy by supporting clinical diagnosis, preoperative planning, and disease monitoring. While U-Net style convolutional neural networks perform well due to their encoder-decoder structures with skip connections, they struggle to capture long-range dependencies. Transformer-based variants address global context but often require heavy computation and large training datasets. This paper proposes GCA-ResUNet, an efficient segmentation network that integrates Grouped Coordinate Attention (GCA) into ResNet-50 residual blocks. GCA uses grouped coordinate modeling to jointly encode global dependencies across channels and spatial locations, strengthening feature representation and boundary delineation while adding minimal parameter and FLOP overhead compared with self-attention. On the Synapse dataset, GCA-ResUNet achieves a Dice score of 86.11%, and on the ACDC dataset, it reaches 92.64%, surpassing several state-of-the-art baselines while maintaining fast inference and favorable computational efficiency. These results indicate that GCA offers a practical way to enhance convolutional architectures with global modeling capability, enabling high-accuracy and resource-efficient medical image segmentation.
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