论文标题
对胸部X射线图像进行深度学习，以检测和评估Covid-19时代的肺炎病例
Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19
论文作者
论文摘要
2019年冠状病毒病（Covid-19）是一种传染病，其首次症状与流感类似。 Covid-19在中国首次出现，很快就传播到世界其他地区，然后导致2019 - 20年冠状病毒大流行。在许多情况下，这种疾病会导致肺炎。由于可以通过射线照相图像观察到肺部感染，因此本文研究了自动分析查询胸部X射线图像的深度学习方法，希望将精确工具带到卫生专业人员筛查Covid-19-19-19和诊断确认的患者。在这种情况下，已经从公开开放的胸部X射线图像集中了培训数据集，深度学习架构和分析策略。提出了量身定制的深度学习模型来检测肺炎感染病例，尤其是病毒病例。假定在流行病中检测到的病毒性肺炎病例在-19情境中具有很高的可能性，即假设COVID 19感染。 Moreover, easy-to-apply health indicators are proposed for estimating infection status and predicting patient status from the detected pneumonia cases. Experimental results show possibilities of training deep learning models over publicly open sets of chest X-ray images towards screening viral pneumonia.通过保留其表演的检测模型，成功诊断出了COVID-19受感染患者的胸部X射线测试图像。通过结合真实和合成健康数据，通过模拟患者的模拟场景来强调拟议健康指标的效率。
Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. COVID-19 appeared first in China and very quickly spreads to the rest of the world, causing then the 2019-20 coronavirus pandemic. In many cases, this disease causes pneumonia. Since pulmonary infections can be observed through radiography images, this paper investigates deep learning methods for automatically analyzing query chest X-ray images with the hope to bring precision tools to health professionals towards screening the COVID-19 and diagnosing confirmed patients. In this context, training datasets, deep learning architectures and analysis strategies have been experimented from publicly open sets of chest X-ray images. Tailored deep learning models are proposed to detect pneumonia infection cases, notably viral cases. It is assumed that viral pneumonia cases detected during an epidemic COVID-19 context have a high probability to presume COVID-19 infections. Moreover, easy-to-apply health indicators are proposed for estimating infection status and predicting patient status from the detected pneumonia cases. Experimental results show possibilities of training deep learning models over publicly open sets of chest X-ray images towards screening viral pneumonia. Chest X-ray test images of COVID-19 infected patients are successfully diagnosed through detection models retained for their performances. The efficiency of proposed health indicators is highlighted through simulated scenarios of patients presenting infections and health problems by combining real and synthetic health data.
