论文标题
剩余的关注U-NET，用于自动多级分割的covid-19胸部CT图像
Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images
论文作者
论文摘要
2019年新型冠状病毒病（Covid-19）一直在世界各地迅速传播，并对公共卫生和经济产生了重大影响。但是，仍然缺乏有关有效量化Covid-19引起的肺部感染的研究。作为诊断框架的基本但具有挑战性的任务，分割在准确量化Covid-19感染中通过计算机断层扫描（CT）图像进行了至关重要的作用。为此，我们提出了一种新型的深度学习算法，用于自动分割多个Covid-19-19感染区域。具体而言，我们使用汇总的残差转换来学习鲁棒和表达的特征表示，并应用软注意机制来提高模型的能力，以区分COVID-19的各种症状。使用公共CT图像数据集，我们与其他竞争方法相比验证了所提出算法的功效。实验结果表明，我们的算法在COVID-19胸部CT图像的自动分割中的出色性能。我们的研究提供了一种有希望的基于深层倾斜的分割工具，为CT图像中COVID-19肺部感染的定量诊断奠定了基础。
The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy. However, there is still lack of studies on effectively quantifying the lung infection caused by COVID-19. As a basic but challenging task of the diagnostic framework, segmentation plays a crucial role in accurate quantification of COVID-19 infection measured by computed tomography (CT) images. To this end, we proposed a novel deep learning algorithm for automated segmentation of multiple COVID-19 infection regions. Specifically, we use the Aggregated Residual Transformations to learn a robust and expressive feature representation and apply the soft attention mechanism to improve the capability of the model to distinguish a variety of symptoms of the COVID-19. With a public CT image dataset, we validate the efficacy of the proposed algorithm in comparison with other competing methods. Experimental results demonstrate the outstanding performance of our algorithm for automated segmentation of COVID-19 Chest CT images. Our study provides a promising deep leaning-based segmentation tool to lay a foundation to quantitative diagnosis of COVID-19 lung infection in CT images.
