皮肤病的计算机辅助诊断（computer-aided diagnosis, CAD）在很大程度上依赖于自动皮肤病变分割，传统的卷积神经网络（convolutional neural network, CNN）在全局上下文信息捕获方面受限，为弥补这一缺陷，现有方法通常采用Transformer替代或与CNN顺序堆叠，但可能导致网络冗余和局部细节丢失。为了解决上述问题，提出了一种基于CNN与Transformer并行的皮肤病变分割网络。采用双分支编码器，并行的将Transformer和CNN相结合，以提高全局上下文建模的效率。其次，创建了一种融合模块将来自于两个分支不同层次的特征有效的融合，同时降低计算复杂度。此外，使用双路径注意力门（dual-path attention gate, DAG）去抑制融合后图像中的噪声以及无关信息，突出重要特征。对ISIC 2017、ISIC 2018和PH²的综合实验表明，所提方法表现出更好地分割效果，且具备良好的泛化能力。

Computer-aided diagnosis (CAD) of skin diseases relies heavily on automatic skin lesion segmentation. Traditional convolutional neural networks (CNN) are limited in capturing global contextual information. To make up for this shortcoming, existing methods usually adopt Transformer replaces or stacks sequentially with CNN, but it may lead to network redundancy and loss of local details. In order to solve the above problems, a parallel skin lesion segmentation network based on CNN and Transformer was proposed. A dual-branch encoder was used to combine Transformer and CNN in parallel to improve the efficiency of global context modeling. Secondly, a fusion module was created to effectively fuse features from different levels of the two branches while reducing computational complexity. In addition, dual-path attention gate (DAG) was used to suppress noise and irrelevant information in the fused image and highlight important features. Comprehensive experiments on ISIC 2017, ISIC 2018 and PH² show that the proposed method achieves better segmentation results and has good generalization ability.