IBM Almaden Research Center（IBM阿尔马登研究中心）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）；MIT Critical Data（麻省理工学院关键数据实验室）；Albert Einstein Healthcare Network-Philadelphia Campus（阿尔伯特·爱因斯坦医疗网络费城院区）；Harvard Medical School（哈佛医学院）
Joy T. Wu, Nkechinyere N. Agu, Ismini Lourentzou, Arjun Sharma, Joseph A. Paguio, Jasper S. Yao, Edward C. Dee, William Mitchell, Satyananda Kashyap, Andrea Gio… 展开作者
Joy T. Wu, Nkechinyere N. Agu, Ismini Lourentzou, Arjun Sharma, Joseph A. Paguio, Jasper S. Yao, Edward C. Dee, William Mitchell, Satyananda Kashyap, Andrea Giovannini, Leo A. Celi, Mehdi Moradi
Despite the progress in automatic detection of radiologic findings from chest X-ray (CXR) images in recent years, a quantitative evaluation of the explainability of these models is hampered by the lack of locally labeled datasets for different findings. With the exception of a few expert-labeled small-scale datasets for specific findings, such as pneumonia and pneumothorax, most of the CXR deep learning models to date are trained on global "weak" labels extracted from text reports, or trained via a joint image and unstructured text learning strategy. Inspired by the Visual Genome effort in the computer vision community, we constructed the first Chest ImaGenome dataset with a scene graph data structure to describe $242,072$ images. Local annotations are automatically produced using a joint rule-based natural language processing (NLP) and atlas-based bounding box detection pipeline. Through a radiologist constructed CXR ontology, the annotations for each CXR are connected as an anatomy-centered scene graph, useful for image-level reasoning and multimodal fusion applications. Overall, we provide: i) $1,256$ combinations of relation annotations between $29$ CXR anatomical locations (objects with bounding box coordinates) and their attributes, structured as a scene graph per image, ii) over $670,000$ localized comparison relations (for improved, worsened, or no change) between the anatomical locations across sequential exams, as well as ii) a manually annotated gold standard scene graph dataset from $500$ unique patients.
Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformers
Imperial College London（帝国理工学院）；Causality in Healthcare AI Hub（医疗AI因果关系中心）；University of Edinburgh（爱丁堡大学）；Cleveland Clinic London（克利夫兰诊所伦敦）；Department of Perioperative Medicine, CHU Clermont-Ferrand（克莱蒙费朗大学医院围手术期医学科）；Department of Medicine, Massachusetts General Hospital（麻省总医院医学部）；Broad Institute of MIT and Harvard（麻省理工学院与哈佛大学博德研究所）
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）
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）
Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance
University of Cambridge（剑桥大学）；Medical University of Vienna（维也纳医科大学）；Cambridge University Hospitals NHS Foundation Trust（剑桥大学医院 NHS 基金会信托）；King's College London（伦敦国王学院）
CheXRelNet: An Anatomy-Aware Model for Tracking Longitudinal Relationships between Chest X-Rays
Virginia Tech（弗吉尼亚理工大学）；IBM Research（IBM研究院）；Institute for Medical Engineering and 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）；Harvard T.H. Chan School of Public Health（哈佛陈曾熙公共卫生学院）；University of British Columbia（不列颠哥伦比亚大学）
When Does Multimodal Learning Help in Healthcare? A Benchmark on EHR and Chest X-Ray Fusion
Department of Computer Science（计算机科学系）；Hong Kong Baptist University（香港 Baptist 大学）；Division of Artificial Intelligence（人工智能 division）；Lingnan University（Lingnan 大学）；School of Nursing（护理学院）；The Hong Kong Polytechnic University（香港理工大学）
COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Images
University of Waterloo（滑铁卢大学）；Waterloo Artificial Intelligence Institute（滑铁卢人工智能研究所）；DarwinAI Corp.（DarwinAI公司）；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（北安大略医学院）
Virtual vs. Reality: External Validation of COVID-19 Classifiers using XCAT Phantoms for Chest Computed Tomography
Duke University School of Medicine（杜克大学医学院）；Duke University（杜克大学）；National Artificial Intelligence Institute, U.S. Dept. of Veterans Affairs（美国退伍军人事务部国家人工智能研究所）
Addressing Asynchronicity in Clinical Multimodal Fusion via Individualized Chest X-ray Generation
School of Nursing, The Hong Kong Polytechnic University（香港理工大学护理学院）；School of Software Engineering, South China University of Technology（华南理工大学软件学院）；Department of Computer Science, Hong Kong Baptist University（香港浸会大学计算机科学系）
针对 EHR 与 CXR 采集不同步导致旧胸片影响临床预测的问题，提出 DDL-CXR，利用既往 CXR 和 EHR 时间序列条件化潜在扩散模型生成最新患者特异性表示，并在 MIMIC 上验证其性能优势。
Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images
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
CONFLUX: A Latent Diffusion Model for 3D Chest-CT Synthesis with RL Post-Training
Department of Biomedical Data Science, Stanford University School of Medicine（斯坦福大学医学院生物医学数据科学系）；Department of Mathematical Modelling, Statistics & Bioinformatics, Ghent University（根特大学数学建模、统计与生物信息学系）；Department of Electrical Engineering, Stanford University（斯坦福大学电气工程系）
CheXthought: A global multimodal dataset of clinical chain-of-thought reasoning and visual attention for chest X-ray interpretation
Center for Artificial Intelligence in Medicine and Imaging（医学与影像人工智能中心）；Department of Radiology（放射科）；Department of Pediatrics（儿科）；Department of Radiology, Weill Cornell Medicine（韦氏 Cornell 医学院放射科）；Department of Radiology, Brigham and Women’s Hospital（布里奇妇产科医院放射科）；University of California, Berkeley（加州大学伯克利分校）；University of California, San Francisco（加州大学旧金山分校）；Department of Biomedical Data Science（生物医学数据科学部）
MonteRET: AI Agent Enhancing Multimodal LLMs with Multi-granularity Knowledge Retrieval for Chest CT Report Generation
Weill Cornell Medicine（威尔康乃尔医学院）；University of Western Australia（西澳大利亚大学）；Indiana University（印第安纳大学）；Regenstrief Institute（瑞根斯特里夫研究所）；Yale University（耶鲁大学）；University of California, Los Angeles（加州大学洛杉矶分校）
Beyond Images: An Integrative Multi-modal Approach to Chest X-Ray Report Generation
CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine（生物医学计算成像与模拟技术中心（CISTIB））；School of Computing, University of Leeds（利兹大学计算机学院）；School of Languages, Cultures and Societies, University of Leeds（利兹大学语言、文化与社会学院）；Kastamonu Training and Research Hospital（卡斯塔莫努培训与研究医院）；School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester（曼彻斯特大学生物、医学与健康学院健康科学学院）；School of Computer Science, Faculty of Science and Engineering, The University of Manchester（曼彻斯特大学科学与工程学院计算机学院）
CheXPO: Preference Optimization for Chest X-ray VLMs with Counterfactual Rationale
The Key Laboratory of Smart Human-Computer Interaction（智能人机交互关键实验室）；Wearable Technology of Shaanxi Province, Xidian University, Xi'an, China（陕西 wearable 技术, 西安电子科技大学, 中国）；Nanjing University of Science and Technology（南京理工大学）
Deep Learning-Based Pneumonia Detection from Chest X-ray Images: A CNN Approach with Performance Analysis and Clinical Implications
Amity Institute of Bio Technology, Amity University Kolkata（阿米蒂大学加尔各答分校阿米蒂生物技术学院）；Dept of Computer Science, Heritage Institute of Technology, Kolkata（加尔各答遗产技术学院计算机科学系）；School of Engineering and Technology, Amity University Kolkata（阿米蒂大学加尔各答分校工程与技术学院）
A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals
University of Minnesota（明尼苏达大学）；Emory University（埃默里大学）；M Health Fairview Informatics（M Health Fairview 信息学）；Indiana University（印第安纳大学）；North Memorial Health Hospital（North Memorial Health 医院）
