PSGR: Pixel-wise Sparse Graph Reasoning for COVID-19 Pneumonia Segmentation in CT Images
Northwestern Polytechnical University（西北工业大学）；Intel Labs（英特尔实验室）；The University of Sydney（悉尼大学）；University of Pittsburgh（匹兹堡大学）；JD Finance America Corporation（京东金融美国公司）
Automated and accurate segmentation of the infected regions in computed tomography (CT) images is critical for the prediction of the pathological stage and treatment response of COVID-19. Several deep convolutional neural networks (DCNNs) have been designed for this task, whose performance, however, tends to be suppressed by their limited local receptive fields and insufficient global reasoning ability. In this paper, we propose a pixel-wise sparse graph reasoning (PSGR) module and insert it into a segmentation network to enhance the modeling of long-range dependencies for COVID-19 infected region segmentation in CT images. In the PSGR module, a graph is first constructed by projecting each pixel on a node based on the features produced by the segmentation backbone, and then converted into a sparsely-connected graph by keeping only K strongest connections to each uncertain pixel. The long-range information reasoning is performed on the sparsely-connected graph to generate enhanced features. The advantages of this module are two-fold: (1) the pixel-wise mapping strategy not only avoids imprecise pixel-to-node projections but also preserves the inherent information of each pixel for global reasoning; and (2) the sparsely-connected graph construction results in effective information retrieval and reduction of the noise propagation. The proposed solution has been evaluated against four widely-used segmentation models on three public datasets. The results show that the segmentation model equipped with our PSGR module can effectively segment COVID-19 infected regions in CT images, outperforming all other competing models.
Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods
Kobe University Graduate School of Medicine（神户大学医学院）；Kyoto University Graduate School of Medicine（京都大学医学院）
CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Images
Manchester Metropolitan University（曼彻斯特城市大学）；Brunel University（布鲁内尔大学）；Whiston Hospital（惠斯顿医院）；St Helens and Knowsley Teaching Hospitals NHS Trust（圣海伦斯和诺斯利教学医院国民保健服务信托）；University of Sheffield（谢菲尔德大学）
Can AI help in screening Viral and COVID-19 pneumonia?
Qatar University（卡塔尔大学）；University of Dhaka（达卡大学）；Hamad General Hospital（哈马德总医院）；North South University（北南大学）；Bangabandhu Sheikh Mujib Medical University（邦班杜·谢赫·穆吉布医科大学）；University of Engineering and Technology, Peshawar（白沙瓦工程技术大学）；National Center for Artificial Intelligence（国家人工智能中心）；Universiti Kebangsaan Malaysia（马来西亚国民大学）
DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays
University of Sheffield（谢菲尔德大学）；Insigneo Institute for in-silico Medicine（Insigneo计算机医学研究所）；University of Oxford（牛津大学）；University of Lincoln（林肯大学）；University of Glasgow（格拉斯哥大学）；University of Brighton（布莱顿大学）；Nantong University（南通大学）
Accurate Recognition of Pneumonia and COVID-19 by Geometric Shape Normalization of Lung Region using Automatic Landmark Detection and Piecewise Affine Warping
Facultad de Ciencias de la Electrónica Benemérita Universidad Autónoma de Puebla（Benemérita自治大学电子科学学院）
Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19
Université de Haute-Alsace（上阿尔萨斯大学）；Université de Strasbourg（斯特拉斯堡大学）；University of Lille（里尔大学）；YNCREA-ISEN Lille（里尔YNCREA-ISEN工程学院）；University of the Basque Country（巴斯克大学）；IKERBASQUE, Basque Foundation for Science（伊克尔巴斯克巴斯克科学基金会）；The University of Tokyo（东京大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；CHU Lille（里尔大学医院）
Enhancing Pneumonia Diagnosis and Severity Assessment through Deep Learning: A Comprehensive Approach Integrating CNN Classification and Infection Segmentation
A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening
Taiwan AI Labs（台湾人工智能实验室）；National Taiwan University Hospital（国立台湾大学医学院附设医院）；Taipei Medical University Hospital（台北医学大学附设医院）；National Health Insurance Administration, Ministry of Health and Welfare（卫生福利部中央健康保险署）；Centers for Disease Control, Ministry of Health and Welfare（卫生福利部疾病管制署）；Taiwan Executive Yuan（台湾行政院）；National Taiwan University College of Medicine（国立台湾大学医学院）
Data Augmentation using Generative Adversarial Networks (GANs) for GAN-based Detection of Pneumonia and COVID-19 in Chest X-ray Images
Institute of Medical Science, University of Toronto（多伦多大学医学科学研究所）；The Hospital for Sick Children（病童医院）；University Health Network（大学健康网络）；Department of Mechanical and Industrial Engineering, University of Toronto（多伦多大学机械与工业工程系）
A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2
Iran University of Science and Technology（伊朗科技大学）；Sharif University of Technology（谢里夫理工大学）
Lung Ultrasound Segmentation and Adaptation between COVID-19 and Community-Acquired Pneumonia
University College London（伦敦大学学院）；Frimley Health NHS Foundation Trust（弗林姆利健康国民医疗服务基金会信托）；Royal Berkshire NHS Foundation Trust（皇家伯克郡国民医疗服务基金会信托）；Hospital de La Santa Creu I Sant Pau（圣克鲁和圣保罗医院）；West Suffolk NHS Foundation Trust（西萨福克国民医疗服务基金会信托）；Cambridge University Hospital（剑桥大学医院）；University of Cambridge（剑桥大学）；Great Ormond Street Children's Hospital NHS Foundation Trust（大奥蒙德街儿童医院国民医疗服务基金会信托）；Institute of Child Health, University College London（伦敦大学学院儿童健康研究所）；UCL Queen Square Institute of Neurology（伦敦大学学院皇后广场神经病学研究所）；University of Pavia（帕维亚大学）
Dense Pixel-Labeling for Reverse-Transfer and Diagnostic Learning on Lung Ultrasound for COVID-19 and Pneumonia Detection
Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）；Louisiana State University（路易斯安那州立大学）；Cosmeticsurg.net, LLC（Cosmeticsurg.net有限责任公司）
FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia
King Abdul Aziz University（阿卜杜勒阿齐兹国王大学）；School of Data Science and Technology, Heilongjiang University（黑龙江大学数据科学与技术学院）；Thapar Institute of Engineering and Technology（塔帕尔工程技术学院）
Advancing Diagnostic Precision: Leveraging Machine Learning Techniques for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest X-Ray Images
Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia
Shanghai Jiao Tong University（上海交通大学）；Shanghai United Imaging Intelligence Co.（上海联影智能有限公司）；Huazhong University of Science and Technology（华中科技大学）；Tongji Hospital（同济医院）；Fudan University（复旦大学）；Shanghai Public Health Clinical Center（上海市公共卫生临床中心）；Central South University（中南大学）；The Second Xiangya Hospital（湘雅二医院）；Jilin University（吉林大学）；China-Japan Union Hospital of Jilin University（吉林大学中日联谊医院）
Machine Learning Automatically Detects COVID-19 using Chest CTs in a Large Multicenter Cohort
Clínica Universidad de Navarra（纳瓦拉大学诊所）；Health Time；Hôpital Foch（福煦医院）；Houston Methodist（休斯顿卫理公会医院）；Donald and Barbara Zucker School of Medicine, Feinstein Institutes for Medical Research, Northwell Health（诺斯韦尔健康唐纳德和芭芭拉·祖克医学院，费因斯坦医学研究所）；Penn Medicine（宾夕法尼亚医学院）；Siemens Healthineers（西门子医疗）；University Hospital Basel（巴塞尔大学医院）；Vancouver General Hospital（温哥华总医院）
Novel coronavirus pneumonia lesion segmentation in CT images
School of Electrical and Automation Engineering, East China Jiaotong University（华东交通大学电气与自动化工程学院）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；Technique Center, Hunan Great Wall Technology Information Company Limited（湖南长城科技信息有限公司技术中心）；School of software, East China Jiaotong University（华东交通大学软件学院）
Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-ray Images
Hangzhou Dianzi University（杭州电子科技大学）；Communication University of Zhejiang（浙江传媒学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Shanghai Jiao Tong University（上海交通大学）
Diagnosing COVID-19 Pneumonia from X-Ray and CT Images using Deep Learning and Transfer Learning Algorithms
Koya University（科亚大学）；Oxford Drug Design（牛津药物设计公司）；Salahaddin University-Erbil（埃尔比勒萨拉赫丁大学）；University of Wolverhampton（伍尔弗汉普顿大学）；King Saud University（沙特国王大学）
New Bag of Deep Visual Words based features to classify chest x-ray images for COVID-19 diagnosis
Deakin University（迪肯大学）
由于严重急性呼吸系统综合征冠状病毒2（COVID-19）的感染会导致肺部出现肺炎样效应，因此胸部X光检查有助于诊断疾病。对于图像的自动分析，它们在机器中由一组语义特征表示。深度学习（DL）模型被广泛用于从图像中提取特征。一般的深度特征可能不适合表示胸部X射线，因为它们具有一些语义区域。尽管基于视觉词袋（BoVW）的特征被证明更适合于X射线类型的图像，但现有的BoVW特征可能无法捕获足够的信息来区分COVID-19感染与其他肺炎相关感染。在本文中，我们提出了一种新的BoVW方法，称为Bag of Deep Visual Words（BoDVW），通过删除特征图归一化步骤并在原始特征图上添加深度特征归一化步骤。这有助于保留每个特征图的语义，这些特征图可能具有区分COVID-19和肺炎的重要线索。我们评估了我们提出的BoDVW特征在使用支持向量机（SVM）诊断COVID-19的胸部X射线分类中的有效性。我们在公开的COVID-19 X射线数据集上的结果表明，与最先进的方法相比，我们的特征可以在更短的计算时间内产生稳定和突出的分类准确性，特别是将COVID-19感染与其他肺炎区分开来。因此，我们的方法可以成为大规模快速诊断COVID-19患者的非常有用的工具。
Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset
Faculty of Computers and Artificial Intelligence, Cairo University（开罗大学计算机与人工智能学院）；MISR Higher Institute for Engineering and Technology（米斯尔高等工程技术学院）；Scientific Research Group in Egypt (SRGE)（埃及科学研究团队）
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection
Northwestern Polytechnical University（西北工业大学）；Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；The University of Adelaide（阿德莱德大学）；GreyBird Ventures, LLC（GreyBird Ventures有限责任公司）；JF Healthcare Inc.（JF Healthcare公司）
Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade oftwo U-nets: training and assessment on multipledatasets using different annotation criteria
Scuola Normale Superiore（比萨高等师范学校）；National Institute of Nuclear Physics (INFN)（国家核物理研究所）；University of Pavia（帕维亚大学）；University of Pisa（比萨大学）；University of Sassari（萨萨里大学）；University of Florence（佛罗伦萨大学）
Reliable COVID-19 Detection Using Chest X-ray Images
Faculty of Information Technology and Communication Sciences, Tampere University（坦佩雷大学信息技术与通信科学学院）；Department of Electrical Engineering, Qatar University（卡塔尔大学电气工程学院）
Deep learning to estimate the physical proportion of infected region of lung for COVID-19 pneumonia with CT image set
Zhejiang University（浙江大学）；the First Affiliated Hospital, School of Medicine, Zhejiang University（浙江大学医学院附属第一医院）；Hangzhou AiSmartVision Co., Ltd.（杭州智视人工智能有限公司）
Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center（西达赛奈医学中心生物医学成像研究所）；Cedars-Sinai Medical Center（西达赛奈医学中心）；Semmelweis University（塞梅尔维斯大学）；Azienda Ospedaliero-Universitaria di Parma（帕尔马大学医院）；Monash Health（莫纳什医疗中心）；Centro Cardiologico Monzino IRCCS, University of Milan（米兰大学蒙齐诺心脏中心IRCCS）；Showa University School of Medicine（昭和大学医学院）
