Abstract：
Characteristics such as low contrast and significant organ shape variations are often exhibited in medical images. The improvement of segmentation performance in medical imaging is limited by the generally insufficient adaptive capabilities of existing attention mechanisms. An efficient Channel Prior Convolutional Attention (CPCA) method is proposed in this paper, supporting the dynamic distribution of attention weights in both channel and spatial dimensions. Spatial relationships are effectively extracted while preserving the channel prior by employing a multi-scale depth-wise convolutional module. The ability to focus on informative channels and important regions is possessed by CPCA. A segmentation network called CPCANet for medical image segmentation is proposed based on CPCA. CPCANet is validated on two publicly available datasets. Improved segmentation performance is achieved by CPCANet while requiring fewer computational resources through comparisons with state-of-the-art algorithms. Our code is publicly available at https://github.com/Cuthbert-Huang/CPCANet.
摘要：
诸如低对比度和显著的器官形状变化的特征通常在医学图像中表现出来。医学成像中分割性能的提高受到现有注意力机制普遍不足的自适应能力的限制。提出了一种有效的信道先验卷积注意(CPCA)方法,支持注意力权重在渠道和空间维度的动态分布。通过采用多尺度深度卷积模块,在保留信道先验的同时有效地提取空间关系。CCPA具有专注于信息渠道和重要区域的能力。提出了一种基于CPCA的医学图像分割网络CPCANet。CPCANet在两个公开可用的数据集上进行了验证。CPCANet实现了改进的分割性能，同时通过与最先进的算法进行比较，需要更少的计算资源。我们的代码可在https://github.com/Cuthbert-Huang/CPCANet上公开获得。
