论文标题
使用胸部CT特征从各种肺部异常出发的Covid-19自动分类
Automated triage of COVID-19 from various lung abnormalities using chest CT features
论文作者
论文摘要
Covid-19的爆发导致全球努力减速流行蔓延。为此，利用了基于胸部计算的训练（CT）筛查和对Covid-19的可疑患者的诊断，可以用作逆转录 - 转录 - 聚合酶链反应（RT-PCR）测试的支持或替代。在本文中，我们提出了一个完全自动化的基于AI的系统，该系统将输入胸部CT扫描和Triages Covid-19 Case。更具体地说，我们产生了多种描述性特征，包括肺部和感染统计，纹理，形状和位置，以训练基于机器学习的分类器，该分类器区分了COVID-19和其他肺部异常（包括社区获得的肺炎）。我们在2191个CT病例的数据集上评估了我们的系统，并在85.4％的特异性（94.0％的ROC-AUC）的敏感性下证明了稳健的解决方案，其灵敏度为90.8％。此外，我们还提供了一项精心设计的特征分析和消融研究，以探讨每个功能的重要性。
The outbreak of COVID-19 has lead to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 suspected patients is utilized, either as a support or replacement to reverse transcription-polymerase chain reaction (RT-PCR) test. In this paper, we propose a fully automated AI based system that takes as input chest CT scans and triages COVID-19 cases. More specifically, we produce multiple descriptive features, including lung and infections statistics, texture, shape and location, to train a machine learning based classifier that distinguishes between COVID-19 and other lung abnormalities (including community acquired pneumonia). We evaluated our system on a dataset of 2191 CT cases and demonstrated a robust solution with 90.8% sensitivity at 85.4% specificity with 94.0% ROC-AUC. In addition, we present an elaborated feature analysis and ablation study to explore the importance of each feature.
