The Impression section of a radiology report summarizes crucial radiology findings in natural language and plays a central role in communicating these findings to physicians. However, the process of generating impressions by summarizing findings is time-consuming for radiologists and prone to errors. We propose to automate the generation of radiology impressions with neural sequence-to-sequence learning. We further propose a customized neural model for this task which learns to encode the study background information and use this information to guide the decoding process. On a large dataset of radiology reports collected from actual hospital studies, our model outperforms existing non-neural and neural baselines under the ROUGE metrics. In a blind experiment, a board-certified radiologist indicated that 67% of sampled system summaries are at least as good as the corresponding human-written summaries, suggesting significant clinical validity. To our knowledge our work represents the first attempt in this direction.
Multilingual Natural Language Processing Model for Radiology Reports -- The Summary is all you need!
ALGORITMI Research Centre/LASI, University of Minho（米尼奥大学 ALGORITMI 研究中心/LASI）；Institute for Artificial Intelligence in Medicine, University Medicine Essen (AöR)（埃森大学医学院人工智能医学研究所）；Department of Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen (AöR)（埃森大学医学院诊断与介入放射学及神经放射学系）；Institute for Computer Graphics and Vision, Graz University of Technology（格拉茨技术大学计算机图形与视觉研究所）
NegBio: a high-performance tool for negation and uncertainty detection in radiology reports
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）；Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health（美国国立卫生研究院临床中心放射与影像科学系）
Word Graph Guided Summarization for Radiology Findings
National University of Defense Technology（国防科技大学）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳市大数据研究院）
Understanding Spatial Language in Radiology: Representation Framework, Annotation, and Spatial Relation Extraction from Chest X-ray Reports using Deep Learning
The University of Texas Health Science Center（德克萨斯大学健康科学中心）；National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）
Improving Radiology Report Conciseness and Structure via Local Large Language Models
Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute（机器学习系，H. Lee Moffitt癌症中心与研究所在读机构）；Deparmtent of Diagnostic Imaging & Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute（诊断影像与介入放射学系，H. Lee Moffitt癌症中心与研究所在读机构）；Department of Radiology, James A. Haley VA Hospital（放射学系，James A. Haley退伍军人医院）
Hierarchical Section Matching Prediction (HSMP) BERT for Fine-Grained Extraction of Structured Data from Hebrew Free-Text Radiology Reports in Crohn's Disease
Faculty of Data and Decision Sciences, Technion – Israel Institute of Technology（1 数据与决策科学学院，技术离子-以色列理工学院）；Faculty of Biomedical Engineering, Technion – Israel Institute of Technology（2 生物医学工程学院，技术离子-以色列理工学院）；Juliet Keidan Institute of Pediatric Gastroenterology, Hepatology and Nutrition, The Eisenberg R&D Authority, Shaare Zedek Medical Center, The Hebrew University School of Medicine（3 肝胆营养研究所，Eisenberg R&D机构，Shaare Zedek医疗中心，希伯来大学医学院）；Pediatric Radiology Unit, Radiology Department, The Eisenberg R&D Authority, Shaare Zedek Medical Center, The Hebrew University of Jerusalem（4 儿童放射科单位，放射科部门，Eisenberg R&D机构，Shaare Zedek医疗中心，希伯来大学杰里科分校）
MGH Radiology Llama: A Llama 3 70B Model for Radiology
School of Computing, University of Georgia（佐治亚大学计算学院）；Department of Radiology, Massachusetts General Hospital and Harvard Medical School（麻省总医院及哈佛医学院放射科）
Beyond Scalar Scores: Exploring LLM-based Metrics for Clinical Significance Evaluation in Radiology Reports
Nanyang Technological University（南洋理工大学）；Technical University of Munich（慕尼黑工业大学）；Alibaba（阿里巴巴）；University of Glasgow（格拉斯哥大学）；University of Massachusetts Boston（马萨诸塞大学波士顿分校）
A Dual-View Approach to Classifying Radiology Reports by Co-Training
University of Alberta（阿尔伯塔大学）；Alberta Machine Intelligence Institute (Amii)（阿尔伯塔机器智能研究所（Amii））；School of Public Health, University of Alberta（阿尔伯塔大学公共卫生学院）；Amii（Amii（阿尔伯塔机器智能研究所））
Calibrated Confidence Expression for Radiology Report Generation
Computer Aided Medical Procedures, Technical University of Munich, Germany（德国慕尼黑工业大学计算机辅助医疗程序）；Munich Center for Machine Learning (MCML), Germany（德国慕尼黑机器学习中心）；Department of Diagnostic and Interventional Radiology, TUM Klinikum rechts der Isar, Germany（德国慕尼黑工业大学伊萨尔河右岸医院诊断与介入放射科）；Department of Diagnostic and Interventional Neuroradiology, TUM Klinikum rechts der Isar, Germany（德国慕尼黑工业大学伊萨尔河右岸医院诊断与介入神经放射科）；Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Germany（德国汉堡-埃彭多夫大学医学中心诊断与介入放射学及核医学科）；AI for Image-Guided Diagnosis and Therapy, Technical University of Munich, Germany（德国慕尼黑工业大学人工智能图像引导诊断与治疗）
PadChest-GR: A Bilingual Chest X-ray Dataset for Grounded Radiology Report Generation
Microsoft Research（微软研究院）；Medbravo；Department of Radiology, University of Cambridge（剑桥大学放射科）；Cambridge University Hospitals NHS Foundation Trust（剑桥大学医院 NHS 基础研究信托）；University Hospital Sant Joan d’Alacant（圣约翰·达·阿尔坎特医院）；Department of Health Informatics, University Hospital Sant Joan d’Alacant（圣约翰·达·阿尔坎特医院健康信息学系）；University Institute for Computing Research, University of Alicante（阿利坎特大学计算研究学院）
Utilizing Longitudinal Chest X-Rays and Reports to Pre-Fill Radiology Reports
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）；National Institutes of Health Clinical Center（美国国立卫生研究院临床中心）；Weill Cornell Medicine（威尔康奈尔医学院）
Automated Labeling of German Chest X-Ray Radiology Reports using Deep Learning
Munich Institute of 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）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；University Hospital, LMU Munich（慕尼黑大学附属医院）；Institute of Neuroradiology, University Hospital, LMU Munich（慕尼黑大学附属医院神经放射学研究所）
ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports
University Hospital, LMU Munich（慕尼黑大学附属医院）；LMU Munich（慕尼黑大学）；Comprehensive Pneumology Center (CPC-M)（综合肺病中心（CPC-M））；German Center for Lung Research (DZL)（德国肺研究中心（DZL））；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心（MCML））
