论文标题
深度学习对胸部CT的冠状病毒检测和分析
Coronavirus Detection and Analysis on Chest CT with Deep Learning
论文作者
论文摘要
新颖的冠状病毒的爆发正式宣布为全球大流行，对我们的日常生活有严重影响。截至撰写本文时，大约有197,188例确认的案件，其中80,881人在“中国大陆”中，死亡人数为7,949，死亡率为3.4％。为了在这项压倒性的挑战中支持放射科医生，我们开发了一种基于深度学习的算法，该算法可以检测，本地化和量化COVID-19的严重程度，从胸部CT扫描中表现出来。该算法由图像处理算法的管道组成，该算法包括肺部分割，2D切片分类和细粒度定位。为了进一步了解该疾病的表现，我们进行了无监督的切片聚类。我们将结果介绍在一个数据集中，该数据集由中国省省省的110名COVID-19患者组成。
The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives. As of this writing there are approximately 197,188 confirmed cases of which 80,881 are in "Mainland China" with 7,949 deaths, a mortality rate of 3.4%. In order to support radiologists in this overwhelming challenge, we develop a deep learning based algorithm that can detect, localize and quantify severity of COVID-19 manifestation from chest CT scans. The algorithm is comprised of a pipeline of image processing algorithms which includes lung segmentation, 2D slice classification and fine grain localization. In order to further understand the manifestations of the disease, we perform unsupervised clustering of abnormal slices. We present our results on a dataset comprised of 110 confirmed COVID-19 patients from Zhejiang province, China.
