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 \url{https://github.com/Cuthbert-Huang/CPCANet}.

翻译：医学图像通常呈现低对比度和器官形态显著变化等特征。现有注意力机制普遍存在自适应能力不足的问题，这限制了医学图像分割性能的提升。本文提出一种高效的通道先验卷积注意力（CPCA）方法，支持在通道和空间维度上动态分配注意力权重。通过采用多尺度深度可分离卷积模块，在保留通道先验的同时有效提取空间关系。CPCA具备关注信息性通道和重要区域的能力。基于CPCA，我们提出了面向医学图像分割的分割网络CPCANet。在两个公开数据集上验证了CPCANet的性能。与最新算法相比，CPCANet在减少计算资源需求的同时实现了更优的分割性能。我们的代码已开源在\url{https://github.com/Cuthbert-Huang/CPCANet}。