LRSAR-Net语义分割模型用于新冠肺炎CT图片辅助诊断

自2019年末新型冠状病毒(Covid-19)疫情在全球爆发以来，世界各国都处于疫情的危害之下。新冠病毒通过入侵人体的呼吸系统，造成肺部感染，甚至死亡。CT(Computed Tomography)图是医生对肺炎患者进行诊断的常规方法。为了提高医生对新冠感染者进行诊断的效率，该文提出一种基于低秩张量自注意力重构的语义分割网络LRSAR-Net，其中低秩张量自注意力重构模块用来获取长范围的信息。低秩张量自注意力重构模块主要包括：低秩张量生成子模块、低秩自注意力子模块、高秩张量重构子模块3个部分。低秩张量自注意力模块先生成多个低秩张量，构建低秩自注意力特征图，然后将多个低秩张量注意力特征图重构成高秩注意力特征图。自注意力模块通过计算相似度矩阵来获取长范围的语义信息。与传统的自注意力模块Non-Local相比，低秩张量自注意力重构模块计算复杂度更低，计算速度更快。最后，该文与其他优秀的语义分割模型进行了对比，体现了模型的有效性。

LRSAR-Net Semantic Segmentation Model for Computer Aided Diagnosis for Covid-19 CT Image

Since the outbreak of the Covid-19 epidemic in the world in late 2019, all countries in the world are under the threat of epidemic. Covid-19 invades the body's respiratory system, causing lung infection or even death. Computed Tomography (CT) is a routine method for doctors to diagnose patients with pneumonia. In order to improve the efficiency of doctors in diagnosing patients with new crown infection, this paper proposes a semantic segmentation network LRSAR-Net based on low rank tensor self-attention reconstruction, in which the low rank tensor self-attention reconstruction module is used to obtain long-range information. The low rank tensor self-attention reconstruction module mainly includes three parts: low rank tensor generation sub module, low rank self-attention sub module and high rank tensor reconstruction module. The low rank tensor self-attention module is divided into multiple low rank tensors, the low rank self-attention feature map is constructed, and then the multiple low rank tensor attention feature maps are reconstructed into a high rank attention feature map. The self-attention module obtains long-range semantic information by calculating the similarity matrix. Compared with the traditional self-attention module Non Local, the low rank tensor self-attention reconstruction module has lower computational complexity and faster computing speed. Finally, this paper compares with other excellent semantic segmentation models to reflect the effectiveness of the model.