Abstract: There are many types of lung diseases and small lesion areas. The existing data sets also have the problem of small data volume, resulting in unsatisfactory model results. In order to improve the diagnosis effect, a lung diagnosis network (ASNet) based on multi-task joint attention is proposed. A multi-task diagnostic network is built based on U-Net, and pathological classification tasks are added to the original lesion segmentation tasks to strengthen the connection between tasks and supplement by segmentation tasks to improve the accuracy of classification tasks; a multi-scale squeeze excitation module is proposed to enhance spatial and information fusion between channels; introduce an axial attention mechanism, emphasizing global context information and location information to alleviate the under-fitting problem caused by the lack of medical data; design an adaptive multi-task hybrid loss function to achieve segmentation and classification tasks Loss weighted equilibrium. Detailed experiments were conducted on the self-built data set. The average results of Dice coefficient, SP,SE, HD and accuracy on the lesion segmentation task were 81.1%, 99.0%,84.1%, 24.6mm and 97.5%, which are better than other advanced segmentation such as SAUNet++ and SwinUnet. Network; in the pathological classification task, the better network (MobileNetV2) improved the Precision, recall and accuracy indicators by 2%, 1.8% and 1.7% respectively. Experiments show that the proposed network improves the accuracy of classification and segmentation, has better segmentation effects on small target lesions, and has a reasonable number of parameters suitable for assisting in the diagnosis of lung diseases.

摘要： 肺部疾病存在种类多、病灶区域小的特点，现有数据集也存在数据量小的问题，导致模型效果不理想。为提高诊断效果，提出一种基于多任务联合注意力机制的肺部诊断网络（ASNet）。基于U-Net构建多任务诊断网络，在原有病灶分割任务基础上加入病理分类任务，加强任务之间的联系以分割任务为辅提升分类任务准确率；提出多尺度挤压激励模块，加强空间和通道之间的信息融合；引入一种轴向注意力机制，强调全局上下文信息和位置信息缓解由于医疗数据匮乏引起的欠拟合问题；设计自适应多任务混合损失函数，实现分割和分类任务损失权重的均衡。在自建数据集上进行了详尽的实验，病灶分割任务上Dice系数、SP、SE、HD和准确率的平均结果为81.1%、99.0%、84.1%、24.6mm和97.5%，优于SAUNet++、SwinUnet等其他先进分割网络；在病理分类任务上比较优网络（MobileNetV2）在Precision、召回率和准确率指标上分别提升了2%、1.8%和1.7%。实验说明所提网络提升了在分类和分割上的精度，对小目标病灶分割效果更佳，合理的参数量适用于协助肺部疾病诊断。