Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment
Massachusetts Institute of Technology（麻省理工学院）；Philips Research North America（飞利浦北美研究院）；Beth Israel Deaconess Medical Center, Harvard Medical School（哈佛医学院贝斯以色列女执事医疗中心）
Geeticka Chauhan, Ruizhi Liao, William Wells, Jacob Andreas, Xin Wang, Seth Berkowitz, Steven Horng, Peter Szolovits, Polina Golland
备注：
The two first authors contributed equally. To be published in the proceedings of MICCAI 2020
We propose and demonstrate a novel machine learning algorithm that assesses pulmonary edema severity from chest radiographs. While large publicly available datasets of chest radiographs and free-text radiology reports exist, only limited numerical edema severity labels can be extracted from radiology reports. This is a significant challenge in learning such models for image classification. To take advantage of the rich information present in the radiology reports, we develop a neural network model that is trained on both images and free-text to assess pulmonary edema severity from chest radiographs at inference time. Our experimental results suggest that the joint image-text representation learning improves the performance of pulmonary edema assessment compared to a supervised model trained on images only. We also show the use of the text for explaining the image classification by the joint model. To the best of our knowledge, our approach is the first to leverage free-text radiology reports for improving the image model performance in this application. Our code is available at https://github.com/RayRuizhiLiao/joint_chestxray.
Semi-supervised Learning for Quantification of Pulmonary Edema in Chest X-Ray Images
Massachusetts Institute of Technology（麻省理工学院）；Philips Research North America（飞利浦北美研究院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard Medical School（哈佛医学院）；Brigham and Women’s Hospital（布莱根妇女医院）
Deep Learning to Quantify Pulmonary Edema in Chest Radiographs
Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard Medical School（哈佛医学院）；Massachusetts Institute of Technology（麻省理工学院）；Philips Research（飞利浦研究院）
Deep learning for detecting pulmonary tuberculosis via chest radiography: an international study across 10 countries
Google Health（谷歌健康）；Advanced Clinical（先进临床）；Apollo Radiology International（阿波罗国际放射学）；Center of Infectious Disease Research in Zambia（赞比亚传染病研究中心）；Sibanye Stillwater（斯班耶-斯蒂尔沃特）；Clickmedix
Automated Estimation of Total Lung Volume using Chest Radiographs and Deep Learning
Radboud university medical center（拉德堡德大学医学中心）
肺总容积是一种重要的定量生物标志物，用于评估限制性肺部疾病。在这项研究中，我们研究了几种深度学习方法的性能，用于自动测量胸片中的总肺容量。从有胸部CT的患者中收集了7621张后前位和侧位胸片（CXR）。同样，从具有肺功能检查（PFT）结果的患者中选择了928项CXR研究。分别根据CT或PFT数据上的肺分割计算参考总肺体积。该数据集用于训练深度学习架构，以根据胸片预测总肺容量。实验以逐步增加复杂性的方式构建，以证明仅使用CT衍生标签进行训练的效果和错误来源。最优模型在291例CXR研究中进行了测试，参考肺体积从PFT获得。最佳深度学习回归模型显示，使用正面和侧面胸片作为输入，MAE为408 ml，MAPE为8.1%，Pearson r = 0.92。CT衍生的标签对于预训练是有用的，但是通过用PFT衍生的标签微调网络来获得最佳性能。我们首次证明，最先进的深度学习解决方案可以从普通胸片中准确测量总肺容量。所提出的模型可用于获得总肺体积从常规采集的胸片在没有额外的成本，并可能是一个有用的工具，以确定随着时间的推移，定期提交胸部X射线的患者的趋势。
Deep LF-Net: Semantic Lung Segmentation from Indian Chest Radiographs Including Severely Unhealthy Images
Bharti School of Telecommunications Technology & Management, Indian Institute of Technology Delhi（印度理工学院德里分校巴蒂电信技术与管理学院）；Teleradiology Solutions（远程放射学解决方案公司）；CSIR-Institute of Genomics and Integrative Biology（CSIR基因组学与整合生物学研究所）；Christian Medical College, Vellore（韦洛尔基督教医学院）
Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system
Radboud University Medical Center（拉德堡德大学医学中心）；Community Health Solutions（社区健康解决方案公司）；Interactive Research and Development（互动研究与发展机构）；The Indus Health Network（印度河健康网络）；Thirona（Thirona公司）；Universal Delft Ltd.（代尔夫特环球有限公司）
DeBoneDiT: Depth-Driven Conditional Bridge Diffusion Transformers for Bone Suppression in Chest X-Ray Images
Zhejiang University（浙江大学）；Hangzhou Dianzi University（杭州电子科技大学）；City University of Hong Kong（香港城市大学）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（国家癌症中心/国家临床医学研究中心/中国医学科学院北京协和医学院肿瘤医院深圳医院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Detecting Pulmonary Coccidioidomycosis (Valley fever) with Deep Convolutional Neural Networks
University of California, Irvine（加利福尼亚大学尔湾分校）；Anivive Lifesciences, Inc.（Anivive生命科学公司）；University of Tennessee（田纳西大学）；The University of Arizona（亚利桑那大学）
GAN-based disentanglement learning for chest X-ray rib suppression
Radboud University Medical Center（拉德堡德大学医学中心）；Netherlands Cancer Institute (NKI)（荷兰癌症研究所）；Z 2 Sky Technologies Inc.（Z 2 Sky 技术公司）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Science and Technology of China（中国科学技术大学）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）
Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays
Lister Hill National Center for Biomedical Communications, National Library of Medicine（美国国家医学图书馆利斯特·希尔国家生物医学通信中心）；Takeda Pharmaceuticals（武田制药）；Saint Louis University（圣路易斯大学）；National Institutes of Health（美国国立卫生研究院）；Walt Whitman High School（沃尔特·惠特曼高中）
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）
A Transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics
The University of Hong Kong（香港大学）；West China Hospital, Sichuan University（四川大学华西医院）；Deepwise Healthcare（深睿医疗）；Zhuhai People’s Hospital and the First Affiliated Hospital of Faculty of Medicine, Macau University of Science and Technology and University Hospital（澳门科技大学医学院附属第一医院、珠海市人民医院及大学医院）；Peking University（北京大学）；Guangzhou Laboratory（广州实验室）；Jinling Hospital, Nanjing University School of Medicine（南京大学医学院附属金陵医院）
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）
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）
Deformable Image Registration of Dark-Field Chest Radiographs for Local Lung Signal Change Assessment
Technical University of Munich（慕尼黑工业大学）；Munich Institute for Biomedical Engineering, Technical University of Munich（慕尼黑工业大学慕尼黑生物医学工程研究所）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学右岸医院）；TUM University Hospital（慕尼黑工业大学大学医院）；Philips Innovative Technologies（飞利浦创新技术公司）；Institute for Advanced Study, Technical University of Munich（慕尼黑工业大学高等研究院）
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）
Comparative Analysis of Deep Learning Architectures for Multi-Disease Classification of Single-Label Chest X-rays
Department of Computer Science, College of Science, Charmo University（查莫大学计算机科学系，科学学院）；Department of Software Engineering, University of Raparin（拉帕林大学软件工程系）
Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification
Centro de Investigación en Computación, Instituto Politécnico Nacional (CIC-IPN)（墨西哥国立理工学院计算机研究中心）；Grand Asian University of Sialkot（锡亚尔科特大亚洲大学）；The Islamia University of Bahawalpur（巴哈瓦尔布尔伊斯兰大学）；Zhejiang Normal University（浙江师范大学）；Leads University Lahore（拉合尔利兹大学）
X-ray2CTPA: Leveraging Diffusion Models to Enhance Pulmonary Embolism Classification
Faculty of Engineering, Tel Aviv University（工程学院，特拉维夫大学）；Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai（数据驱动与数字医学部，医学部，Mount Sinai医学院）；Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Israel（诊断影像科，Sheba 医疗中心，Ramat Gan，以色列）；Department of Radiology, Icahn School of Medicine, Mount Sinai（放射科，Icahn医学院，Mount Sinai）
