Computer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human-machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity.
翻译:计算机辅助诊断已成为准确和立即检测2019年冠状病毒病(COVID-19-19)的必要手段,有助于治疗和防止病毒传播。许多研究提议使用深学习技术进行COVID-19诊断。然而,它们使用有限的胸前X光(CXR)图像储存库进行评价,使用少量的、几百个COVID-19样本。此外,这些方法既不能将COVID-19-19感染的严重程度本地化,也不能分级。为此目的,最近建议探索深网络启动图的研究,但是,这些研究仍然不准确,因为将实际感染点本地化,使其不适于临床使用。这项研究提议采用一种新的方法,通过制作所谓的感染图,将CXR19图像中的COVI-19进行联合定位、严重程度分级测试和检测。为了做到这一点,我们编集了119,316 CXRCX图像的最大数据集,其中包括2951 COVID-19D-19样本,其中提议的地面图解分解面部部分是CXRD的分辨点。此外,我们通过新的协作性人类-RD分解方法,将C-D分级的分级的分级的分解数据公开释放。最后,我们用FX分级的分级的分级的分级的分级的分解为受感染的C-C-C-C-C-D。