论文标题
COVID事实：一个完全自动化的胶囊网络网络框架，用于识别胸部CT扫描的COVID-19病例
COVID-FACT: A Fully-Automated Capsule Network-based Framework for Identification of COVID-19 Cases from Chest CT scans
论文作者
论文摘要
The newly discovered Corona virus Disease 2019 (COVID-19) has been globally spreading and causing hundreds of thousands of deaths around the world as of its first emergence in late 2019. Computed tomography (CT) scans have shown distinctive features and higher sensitivity compared to other diagnostic tests, in particular the current gold standard, i.e., the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test.当前基于深度学习的算法主要是基于卷积神经网络（CNN）开发的，以鉴定Covid-19-19肺炎病例。但是，CNN需要大量的数据增强和大型数据集，以确定图像实例之间的详细空间关系。此外，使用CT扫描的现有算法，要么使用简单的阈值机制将切片级别的预测扩展到患者级的预测，要么依靠复杂的感染分割来识别疾病。在本文中，我们提出了一个两阶段完全自动化的基于CT的框架，用于识别所谓的“ covid fact”的covid-19阳性病例。 Covid-Fact利用胶囊网络作为其主要构建块，因此能够捕获空间信息。特别是，为了使所提出的共同事实独立于感染区域的复杂分割，在第一阶段被检测到表明感染的切片，第二阶段将导致将患者分类为共证和非卵泡病例。 Covid-Fact检测出感染的切片，并使用内部CT扫描数据集鉴定阳性Covid-19病例，其中包含COVID-19，社区获得的肺炎和正常病例。根据我们的实验，Covid-Fact的精度为90.82％，灵敏度为94.55％，特异性为86.04％，曲线（AUC）下的面积为0.98，同时根据其对手的监督和注释较少。
The newly discovered Corona virus Disease 2019 (COVID-19) has been globally spreading and causing hundreds of thousands of deaths around the world as of its first emergence in late 2019. Computed tomography (CT) scans have shown distinctive features and higher sensitivity compared to other diagnostic tests, in particular the current gold standard, i.e., the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Current deep learning-based algorithms are mainly developed based on Convolutional Neural Networks (CNNs) to identify COVID-19 pneumonia cases. CNNs, however, require extensive data augmentation and large datasets to identify detailed spatial relations between image instances. Furthermore, existing algorithms utilizing CT scans, either extend slice-level predictions to patient-level ones using a simple thresholding mechanism or rely on a sophisticated infection segmentation to identify the disease. In this paper, we propose a two-stage fully-automated CT-based framework for identification of COVID-19 positive cases referred to as the "COVID-FACT". COVID-FACT utilizes Capsule Networks, as its main building blocks and is, therefore, capable of capturing spatial information. In particular, to make the proposed COVID-FACT independent from sophisticated segmentation of the area of infection, slices demonstrating infection are detected at the first stage and the second stage is responsible for classifying patients into COVID and non-COVID cases. COVID-FACT detects slices with infection, and identifies positive COVID-19 cases using an in-house CT scan dataset, containing COVID-19, community acquired pneumonia, and normal cases. Based on our experiments, COVID-FACT achieves an accuracy of 90.82%, a sensitivity of 94.55%, a specificity of 86.04%, and an Area Under the Curve (AUC) of 0.98, while depending on far less supervision and annotation, in comparison to its counterparts.
