Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images
Nanjing University（南京大学）；National Institute of Healthcare Data Science at Nanjing University（南京大学医疗健康数据科学国家研究院）；Central South University（中南大学）；Second Xiangya Hospital, Central South University（中南大学湘雅二医院）；University of North Carolina（北卡罗来纳大学）
Kelei He, Wei Zhao, Xingzhi Xie, Wen Ji, Mingxia Liu, Zhenyu Tang, Feng Shi, Yang Gao, Jun Liu, Junfeng Zhang, Dinggang Shen
Understanding chest CT imaging of the coronavirus disease 2019 (COVID-19) will help detect infections early and assess the disease progression. Especially, automated severity assessment of COVID-19 in CT images plays an essential role in identifying cases that are in great need of intensive clinical care. However, it is often challenging to accurately assess the severity of this disease in CT images, due to variable infection regions in the lungs, similar imaging biomarkers, and large inter-case variations. To this end, we propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images, by jointly performing lung lobe segmentation and multi-instance classification. Considering that only a few infection regions in a CT image are related to the severity assessment, we first represent each input image by a bag that contains a set of 2D image patches (with each cropped from a specific slice). A multi-task multi-instance deep network (called M$^2$UNet) is then developed to assess the severity of COVID-19 patients and also segment the lung lobe simultaneously. Our M$^2$UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment (with a unique hierarchical multi-instance learning strategy). Here, the context information provided by segmentation can be implicitly employed to improve the performance of severity assessment. Extensive experiments were performed on a real COVID-19 CT image dataset consisting of 666 chest CT images, with results suggesting the effectiveness of our proposed method compared to several state-of-the-art methods.
COVID-Net S: Towards computer-aided severity assessment via training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity
University of Waterloo（滑铁卢大学）；DarwinAI Corp.（DarwinAI公司）；Stony Brook School of Medicine（石溪大学医学院）；Montefiore Medical Center（蒙蒂菲奥里医疗中心）；Albert Einstein College of Medicine（阿尔伯特·爱因斯坦医学院）
Vision Transformer-based Model for Severity Quantification of Lung Pneumonia Using Chest X-ray Images
University of the Basque Country（巴斯克大学）；IKERBASQUE；Ho Chi Minh City Open University（胡志明市开放大学）；University of Strasbourg（斯特拉斯堡大学）；INSERM（法国国家健康与医学研究院）；Université de Haute-Alsace（上阿尔萨斯大学）
本文提出 ViTReg-IP，一种由 ViT 与回归头构成的轻量模型，用于根据胸部 X 光量化 COVID-19 等肺病严重程度，并在多来源数据集上验证其低成本、高性能与泛化性。
TMF-RSE: Tri-Modal Fusion with Regional Semantics and Evidential Uncertainty for Lung Severity Scoring
Institute of Applied Sciences and Intelligent Systems (ISASI), CNR（应用科学与智能系统研究所（ISASI），CNR）；Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU)（巴斯克国家大学计算机科学与人工智能系）；University of Salento（萨莱nton大学）
Incremental Value and Interpretability of Radiomics Features of Both Lung and Epicardial Adipose Tissue for Detecting the Severity of COVID-19 Infection
School of Computer and Communication Engineering, Zhengzhou University of Light Industry（郑州轻工业大学计算机与通信工程学院）；Universidade Federal Fluminense（弗鲁米嫩塞联邦大学）；DASA Complexo Hospitalar de Niterói（达萨尼泰罗伊医院综合体）；Michigan Technological University（密歇根理工大学）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）；Shanghai Institute of Cardiovascular Diseases（上海市心血管病研究所）；National Clinical Research Center for Interventional Medicine（国家介入医学临床研究中心）；Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University（密歇根理工大学生物计算与数字健康中心、计算与网络系统研究所及健康研究所）
Deep learning automated quantification of lung disease in pulmonary hypertension on CT pulmonary angiography: A preliminary clinical study with external validation
University of Sheffield（谢菲尔德大学）；Sheffield Teaching Hospitals NHS Foundation Trust（谢菲尔德教学医院NHS基金会信托）；INSIGNEO, Institute for In Silico Medicine, University of Sheffield（谢菲尔德大学INSIGNEO计算医学研究所）；NIHR Sheffield Biomedical Research Centre, University of Sheffield（谢菲尔德大学NIHR谢菲尔德生物医学研究中心）
Automated assessment of disease severity of COVID-19 using artificial intelligence with synthetic chest CT
The First Affiliated Hospital of USTC（中国科学技术大学附属第一医院）；University of Science and Technology of China（中国科学技术大学）；University of Science and Technology（科技大学）；The first affiliated hospital of Bengbu medical college（蚌埠医学院第一附属医院）；AItour（AItour公司）
Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images
Beihang University（北京航空航天大学）；Central South University（中南大学）；The Second Xiangya Hospital, Central South University（中南大学湘雅二医院）；First hospital of Changsha（长沙市第一医院）；Changsha public health treatment center（长沙市公共卫生救治中心）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）
Rapid Lung Ultrasound COVID-19 Severity Scoring with Resource-Efficient Deep Feature Extraction
University College London（伦敦大学学院）；Frimley Health NHS Foundation Trust（弗林利健康NHS基金会信托）；Royal Berkshire NHS Foundation Trust（皇家伯克希尔NHS基金会信托）；Hospital de La Santa Creu I Sant Pau（圣十字圣保罗医院）；West Suffolk NHS Foundation Trust（西萨福克NHS基金会信托）；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NHS基金会信托）；University of Pavia（帕维亚大学）
covEcho Resource constrained lung ultrasound image analysis tool for faster triaging and active learning
Indian Institute of Technology Palakkad（印度理工学院帕拉卡德分校）；Hospital Universitario Puerta de Hierro（普埃尔塔·德·耶罗大学医院）；Sree Chitra Tirunal Institute for Medical Sciences & Technology（斯里·奇特拉·蒂鲁纳尔医学科学与技术研究所）；Government Medical College Thiruvananthapuram（特里凡得琅政府医学院）
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）
Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan Images
St. Joseph’s College of Engineering（圣约瑟夫工程学院）；Techno India College of Technology（泰克诺印度技术学院）；Shantou University（汕头大学）；Cairo University（开罗大学）；University of South Dakota（南达科他大学）
Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning
University of Texas at Arlington（德克萨斯大学阿灵顿分校）；RIKEN（理化学研究所）；University of Tokyo（东京大学）；National Institute of Health Clinical Center（美国国立卫生研究院临床中心）；National Cancer Center Research Institute（国立癌症研究中心研究所）
COVID-19 Infection Localization and Severity Grading from Chest X-ray Images
Qatar University（卡塔尔大学）；Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Reem Medical Center（Reem医疗中心）；Hamad General Hospital（哈马德综合医院）；Weill Cornell Medicine - Qatar（威尔康奈尔医学院卡塔尔分校）
Deep Learning-based Four-region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis
Seoul National University Hospital（首尔大学医院）；Massachusetts General Hospital（麻省总医院）；Kyungpook National University（庆北国立大学）；Yeungnam University College of Medicine（岭南大学医学院）；Catholic University of Daegu School of Medicine（大邱加图立大学医学院）；Keimyung University School of Medicine（启明大学医学院）
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）
Severity Quantification and Lesion Localization of COVID-19 on CXR using Vision Transformer
Korea Advanced Institute of Science and Technology（韩国科学技术院）；Asan Medical Center（峨山医院）；University of Ulsan College of Medicine（蔚山大学医学院）；College of Medicine, Chungnam National Univerity（忠南国立大学医学院）；College of Medicine, Yeungnam University（岭南大学医学院）；School of Medicine, Kyungpook National University（庆北国立大学医学院）
Deep learning-based lung segmentation and automatic regional template in chest X-ray images for pediatric tuberculosis
Universidad Politécnica de Madrid（马德里理工大学）；CIBER-BBN（生物工程、生物材料与纳米医学网络研究中心）；Children’s National Hospital（国家儿童医院）；George Washington University School of Medicine（乔治华盛顿大学医学院）；ISGlobal（巴塞罗那全球健康研究所）；Hospital Clínic（巴塞罗那临床医院）；Universitat de Barcelona（巴塞罗那大学）；Stellenbosch University（斯泰伦博斯大学）
CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）；Kharkevich Institute for Information Transmission Problems（哈尔克维奇信息传输问题研究所）；Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department（莫斯科卫生局诊断与远程医疗技术研究实践临床中心）；Sklifosovsky Clinical and Research Institute for Emergency Medicine（斯克利福索夫斯基急救医学临床研究所）
Towards Automated COVID-19 Presence and Severity Classification
Faculty of Applied Computer Science, University of Augsburg（奥格斯堡大学应用计算机科学学院）；Institute for Digital Medicine, University Hospital Augsburg（奥格斯堡大学医院数字医学研究所）
COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Automated Diagnosis and Severity Assessment of COVID-19
The University of Sydney（悉尼大学）；Shanghai Jiao Tong University（上海交通大学）；Shandong First Medical University（山东第一医科大学）；Shandong Academy of Medical Sciences（山东省医学科学院）；Shandong Cancer Hospital and Institute（山东省肿瘤医院）
Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT
Hôpital Foch（福煦医院）；Donald and Barbara Zucker School of Medicine（唐纳德和芭芭拉·祖克医学院）；Feinstein Institutes for Medical Research（范斯坦医学研究所）；Northwell Health（诺斯韦尔健康中心）；Siemens Healthineers（西门子医疗）；University Hospital Basel（巴塞尔大学医院）；Clinic of Radiology & Nuclear medicine（放射与核医学科）；Vancouver General Hospital（温哥华总医院）
MobileCaps: A Lightweight Model for Screening and Severity Analysis of COVID-19 Chest X-Ray Images
National Institute of Technology Karnataka（卡纳塔克邦国立理工学院）；Kasturba Medical College and Hospital, Manipal（马尼帕尔卡斯图巴医学院及医院）；Manipal Academy of Higher Education（马尼帕尔高等教育学院）；J.S.S. Medical College, Mysore（迈索尔J.S.S.医学院）；JSS Academy of Higher Education and Research（JSS高等教育与研究学院）
SCUNet++: Swin-UNet and CNN Bottleneck Hybrid Architecture with Multi-Fusion Dense Skip Connection for Pulmonary Embolism CT Image Segmentation
Hangzhou Dianzi University（杭州电子科技大学）；Second Affiliated Hospital of the Chinese University of Hong Kong（香港中文大学第二附属医院）；Shenzhen Research Institute of Big Data（深圳市大数据研究院）
