医学图像分割和分类是计算机辅助临床诊断中最关键的两个步骤。通常应该以适当的方式对兴趣区域进行分割，以提取用于进一步疾病分类的有用特征。然而，这些方法在计算复杂度和时间消耗上比较高。在本文中，我们提出了一种一阶段多任务注意力网络（MTANet），它在对图像中的物体进行高效分类的同时为每个医学物体生成高质量的分割掩模。在分割任务中，设计了一个反向加法注意力模块，用于融合全局地图中的区域和高分辨率特征中的边界线索，并在分类任务中使用了一个注意力瓶颈模块，用于特征融合。我们通过基于CNN和Transformer的架构在三种成像模态下对MTANet的性能进行了评估：CVC-ClinicDB数据集用于息肉分割，ISIC-2018数据集用于皮肤病变分割，以及我们的私有超声数据集用于肝肿瘤分割和分类。我们的模型在所有三个数据集上都优于最先进模型，并且在肝肿瘤诊断中优于所有25名放射科医生。

Medical image segmentation and classification are two of the most key steps in computer-aided clinical diagnosis. The region of interest were usually segmented in a proper manner to extract useful features for further disease classification. However, these methods are computationally complex and time-consuming. In this paper, we proposed a one-stage multi-task attention network (MTANet) which efficiently classifies objects in an image while generating a high-quality segmentation mask for each medical object. A reverse addition attention module was designed in the segmentation task to fusion areas in global map and boundary cues in high-resolution features, and an attention bottleneck module was used in the classification task for image feature and clinical feature fusion. We evaluated the performance of MTANet with CNN-based and transformer-based architectures across three imaging modalities for different tasks: CVC-ClinicDB dataset for polyp segmentation, ISIC-2018 dataset for skin lesion segmentation, and our private ultrasound dataset for liver tumor segmentation and classification. Our proposed model outperformed state-of-the-art models on all three datasets and was superior to all 25 radiologists for liver tumor diagnosis.