With the rise and ever-increasing potential of deep learning techniques in recent years, publicly available medical datasets became a key factor to enable reproducible development of diagnostic algorithms in the medical domain. Medical data contains sensitive patient-related information and is therefore usually anonymized by removing patient identifiers, e.g., patient names before publication. To the best of our knowledge, we are the first to show that a well-trained deep learning system is able to recover the patient identity from chest X-ray data. We demonstrate this using the publicly available large-scale ChestX-ray14 dataset, a collection of 112,120 frontal-view chest X-ray images from 30,805 unique patients. Our verification system is able to identify whether two frontal chest X-ray images are from the same person with an AUC of 0.9940 and a classification accuracy of 95.55%. We further highlight that the proposed system is able to reveal the same person even ten and more years after the initial scan. When pursuing a retrieval approach, we observe an mAP@R of 0.9748 and a precision@1 of 0.9963. Furthermore, we achieve an AUC of up to 0.9870 and a precision@1 of up to 0.9444 when evaluating our trained networks on external datasets such as CheXpert and the COVID-19 Image Data Collection. Based on this high identification rate, a potential attacker may leak patient-related information and additionally cross-reference images to obtain more information. Thus, there is a great risk of sensitive content falling into unauthorized hands or being disseminated against the will of the concerned patients. Especially during the COVID-19 pandemic, numerous chest X-ray datasets have been published to advance research. Therefore, such data may be vulnerable to potential attacks by deep learning-based re-identification algorithms.
Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray
Città della Salute e della Scienza Hospital（健康与科学城医院）；University of Turin（都灵大学）；Civil Hospital（平民医院）；Candiolo Cancer Institute, FPO-IRCCS（坎迪奥洛癌症研究所，FPO-IRCCS）；Ospedale San Giovanni Bosco（圣乔瓦尼·博斯科医院）
Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts
University of Michigan（密歇根大学）；Institute for Healthcare Policy & Innovation（医疗政策与创新研究所）；Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）；Department of Radiology（放射科）；Department of Internal Medicine（内科）
EVOKE: Elevating Chest X-ray Report Generation via Multi-View Contrastive Learning and Patient-Specific Knowledge
Xidian University（西安电子科技大学）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；Ruijin Hospital（瑞金医院）；Shanghai Institute of Traumatology and Orthopedics（上海市伤骨科研究所）；Harbin Medical University（哈尔滨医科大学）；The First Affiliated Hospital of Harbin Medical University（哈尔滨医科大学附属第一医院）；Brown University（布朗大学）
Study of Different Deep Learning Approach with Explainable AI for Screening Patients with COVID-19 Symptoms: Using CT Scan and Chest X-ray Image Dataset
University of Oklahoma（俄克拉荷马大学）；University of Memphis（孟菲斯大学）；Marquette University（马凯特大学）
Assessing the Performance of Automated Prediction and Ranking of Patient Age from Chest X-rays Against Clinicians
University of Warwick（华威大学）；Guy’s and St Thomas’ NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托）；University Hospitals Coventry and Warwickshire NHS Trust（考文垂与沃里克郡大学医院国民保健服务信托）；Guy’s and St Thomas’ NHS Trust（盖伊和圣托马斯国民保健服务信托）；King’s College London（伦敦国王学院）；The Alan Turing Institute（艾伦·图灵研究所）
Indian Institute of Technology Delhi（德里印度理工学院）；University of Bath（巴斯大学）；Indian Institute of Technology Kanpur（坎普尔印度理工学院）；All India Institute of Medical Sciences Delhi（德里全印度医学科学研究所）
WisdomNet: Prognosis of COVID-19 with Slender Prospect of False Negative Cases and Vaticinating the Probability of Maturation to ARDS using Posteroanterior Chest X-Rays
G.B. Pant Government Engineering College（G.B. Pant政府工程学院）
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。
A Generalizable Artificial Intelligence Model for COVID-19 Classification Task Using Chest X-ray Radiographs: Evaluated Over Four Clinical Datasets with 15,097 Patients
University of Wisconsin（威斯康星大学）；Henry Ford Health（亨利福特医疗集团）
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）
Triage of Potential COVID-19 Patients from Chest X-ray Images using Hierarchical Convolutional Networks
CONNECT Centre, Trinity College Dublin（都柏林圣三一大学CONNECT中心）；University of Sindh（信德大学）；Signy Advanced Technologies（西格尼先进技术公司）；Indian Institute of Technology, Dehli（印度理工学院德里分校）；King Faisal University（费萨尔国王大学）
BIO-CXRNET: A Robust Multimodal Stacking Machine Learning Technique for Mortality Risk Prediction of COVID-19 Patients using Chest X-Ray Images and Clinical Data
Qatar University（卡塔尔大学）；North South University（北南大学）；University of South Carolina（南卡罗来纳大学）；Hamad General Hospital（哈马德综合医院）；AFG College with the University of Aberdeen（阿伯丁大学AFG学院）
AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study
University Campus Bio-Medico of Rome（罗马生物医学自由大学）；Centro Diagnostico Italiano S.p.A.（意大利诊断中心股份公司）；Istituto Italiano di Tecnologia（意大利理工学院）；Bracco Imaging S.p.A.（博莱科影像股份公司）；Sapienza University of Rome（罗马大学）；Fondazione IRCCS Policlinico San Matteo（圣马泰奥综合医院IRCCS基金会）
MDF-Net for abnormality detection by fusing X-rays with clinical data
Queensland University of Technology（昆士兰科技大学）；Lusíadas Knowledge Center（卢西亚达斯知识中心）；Instituto de Sistemas e Robótica（系统与机器人研究所）；Instituto Superior Técnico, University of Lisbon（里斯本大学高等技术学院）；INESC-ID；Data61 CSIRO
Wayne State Univ.（韦恩州立大学）；NanoBioScience Institute, Wayne State Univ.（韦恩州立大学纳米生物科学研究所）；University of Michigan（密歇根大学）；Henry Ford Health System（亨利福特医疗系统）
COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images
University of Waterloo（滑铁卢大学）；DarwinAI Corp.（DarwinAI公司）；Niagara Health（尼亚加拉医疗）；McMaster University（麦克马斯特大学）；Thunder Bay Regional Health Sciences Centre（桑德贝地区健康科学中心）；Northern Ontario School of Medicine（北部安大略医学院）
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）
Bone Suppression on Chest Radiographs With Adversarial Learning
Imaging Biomarkers and Computer-aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center（美国国立卫生研究院临床中心放射学与成像科学部成像生物标志物和计算机辅助诊断实验室）；Ping An Technology Co., Ltd.（平安科技有限公司）
MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs
Institute of Medical Engineering & Science, Massachusetts Institute of Technology（麻省理工学院医学工程与科学研究所）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Stanford University（斯坦福大学）；Harvard Medical School（哈佛医学院）；National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆国家生物技术信息中心）
胸部X线摄影是一种非常强大的成像方式，可以对患者的胸部进行详细检查，但需要经过专门的培训才能进行正确的解释。随着高性能通用计算机视觉算法的出现，胸部X光片的精确自动分析越来越受到研究人员的关注。然而，开发这些技术的一个关键挑战是缺乏足够的数据。在这里，我们描述了MIMIC-CXR-JPG v2.0.0，这是一个大型数据集，包含377，110张胸部X光片，与2011 - 2016年间来自Beth Israel Deaconess Medical Center的227，827项成像研究相关。图像提供有14个标签，这些标签来自应用于相应自由文本放射学报告的两个自然语言处理工具。MIMIC-CXR-JPG完全来自MIMIC-CXR数据库，旨在提供MIMIC-CXR的方便处理版本，并为数据分割和图像标签提供标准参考。所有图像均已去识别化，以保护患者隐私。该数据集免费提供，以促进和鼓励医学计算机视觉的广泛研究。
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）
