Microsoft Research Health Futures（微软研究院健康未来部门）；Microsoft Research India（微软研究院印度分部）；Microsoft Azure AI（微软Azure人工智能部门）；Microsoft Health and Life Sciences（微软健康与生命科学部门）；Department of Radiology, Addenbrooke’s Hospital, Cambridge University Hospitals（剑桥大学医院艾登布鲁克医院放射科）
Shruthi Bannur, Kenza Bouzid, Daniel C. Castro, Anton Schwaighofer, Anja Thieme, Sam Bond-Taylor, Maximilian Ilse, Fernando Pérez-García, Valentina Salvatelli, … 展开作者
Shruthi Bannur, Kenza Bouzid, Daniel C. Castro, Anton Schwaighofer, Anja Thieme, Sam Bond-Taylor, Maximilian Ilse, Fernando Pérez-García, Valentina Salvatelli, Harshita Sharma, Felix Meissen, Mercy Ranjit, Shaury Srivastav, Julia Gong, Noel C. F. Codella, Fabian Falck, Ozan Oktay, Matthew P. Lungren, Maria Teodora Wetscherek, Javier Alvarez-Valle, Stephanie L. Hyland
备注：
72 pages, 21 figures. v2 updates the model and adds results on the PadChest-GR dataset
Radiology reporting is a complex task requiring detailed medical image understanding and precise language generation, for which generative multimodal models offer a promising solution. However, to impact clinical practice, models must achieve a high level of both verifiable performance and utility. We augment the utility of automated report generation by incorporating localisation of individual findings on the image - a task we call grounded report generation - and enhance performance by incorporating realistic reporting context as inputs. We design a novel evaluation framework (RadFact) leveraging the logical inference capabilities of large language models (LLMs) to quantify report correctness and completeness at the level of individual sentences, while supporting the new task of grounded reporting. We develop MAIRA-2, a large radiology-specific multimodal model designed to generate chest X-ray reports with and without grounding. MAIRA-2 achieves state of the art on existing report generation benchmarks and establishes the novel task of grounded report generation.
MAIRA-Seg: Enhancing Radiology Report Generation with Segmentation-Aware Multimodal Large Language Models
Microsoft Health Futures（微软健康未来）；Microsoft Research India（微软研究院印度分院）；University of Cambridge（剑桥大学）；Cambridge University Hospitals NHS Foundation Trust（剑桥大学医院国民保健信托基金）
MAIRA-1: A specialised large multimodal model for radiology report generation
Health Futures, Microsoft Research（微软研究院健康未来部门）；Microsoft Research India（微软研究院印度分部）；Microsoft Azure AI（微软Azure人工智能）；Microsoft Health and Life Sciences（微软健康与生命科学部门）
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）
AlphaRAD: Grounded Zero-Shot Classification in Chest Radiology via $α$-Corrected Binary Cross Entropy and Factorized Latent Supervision
Peter Munk Cardiac Centre, University Health Network (UHN)（彼得·芒克心脏中心，大学健康网络（UHN））；Ted Rogers Centre for Heart Research（泰德·罗杰斯心脏研究中心）；University of Toronto (U of T)（多伦多大学）；Vector Institute（向量研究所）；Toronto General Hospital Research Institute, UHN（多伦多总医院研究所，大学健康网络（UHN））
CCS: Clinical Consensus Selection for Radiology Report Generation
School of Computing Science, University of Glasgow（格拉斯哥大学计算机科学学院）；School of Electrical and Computer Engineering, University of Sydney（悉尼大学电气与计算机工程学院）；Language Technology Lab, University of Cambridge（剑桥大学语言技术实验室）
Clinically Grounded Agent-based Report Evaluation: An Interpretable Metric for Radiology Report Generation
New York University（纽约大学）；NYU Langone Health（纽约大学兰格医学中心）；Genentech（基因泰克）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；Wenzhou Medical University（温州医科大学）；Macau University of Science and Technology（澳门科学大学）
CRIMSON: A Clinically-Grounded LLM-Based Metric for Generative Radiology Report Evaluation
Department of Biomedical Informatics, Harvard Medical School（生物医学信息学系，哈佛医学院）；Department of Radiation Oncology, Mass General Brigham（放射肿瘤科，麻省总医院与布里奇沃特医院）；King Fahad Hospital, Al-Ahsa Health Cluster, Al Hofuf, Saudi Arabia（国王法赫德医院，阿尔阿萨卫生集群，阿尔霍夫，沙特阿拉伯）；Ras-Tanura General Hospital, Ministry of Health, Eastern Province, Saudi Arabia（拉斯-坦纳医院，卫生部，东部省，沙特阿拉伯）；Department of Medical Imaging, King Abdulaziz Medical City, Ministry of National Guard, Riyadh, Saudi Arabia（医学影像科，国王阿卜杜勒-阿齐兹医疗城，国民卫队部，利雅得，沙特阿拉伯）
Phrase-grounded Fact-checking for Automatically Generated Chest X-ray Reports
Rensselaer Polytechnic Institute, NY, USA（罗文学院）；IBM Research, Almaden, CA, USA（IBM研究院）；Stanford University, CA, USA（斯坦福大学）；Massachusetts General Hospital (MGH), Boston, USA（麻省总医院）
SGRNet: Spatially Guided Radiology Network for Structured Radiological Reporting of Head and Neck Cancer
University of Strasbourg（斯特拉斯堡大学）；IHU Strasbourg（斯特拉斯堡大学医院研究所）；Indian Institute of Technology (IIT) Madras（印度马德拉斯理工学院）；All India Institute of Medical Sciences（全印度医学科学院）
NeuroSymb-MRG: Differentiable Abductive Reasoning with Active Uncertainty Minimization for Radiology Report Generation
University of Macau（澳门大学）；Tsinghua University（清华大学）；Fudan University（复旦大学）；Peking University（北京大学）；The First People’s Hospital of Foshan（佛山市第一人民医院）；Capital Medical University（首都医科大学）；Rutgers Cancer Institute, NJ, USA（罗格斯癌症研究所（美国新泽西州）；纽约大学朗格尼健康中心（美国纽约州））；and NYU Langone Health, NY, USA（Techno国际新城（印度加尔各答））；Techno International New Town, Kolkata, India
Structure is Supervision: Multiview Masked Autoencoders for Radiology
Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系）；Department of Computer Science, UC Irvine（加州大学尔湾分校计算机科学系）；Department of Quantitative Biomedicine, University of Zurich（苏黎世大学定量生物医学系）
PanDent: Toward Comprehensive Tooth-Level Structure-Language Consistency in Dental Radiology
Faculty of Dentistry, The University of Hong Kong（香港大学牙医学院）；Imperial College London（帝国理工学院）；University of Science and Technology of China（中国科学技术大学）；Department of Data and Systems Engineering, The University of Hong Kong（香港大学数据与系统工程系）；Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）
CogRad: A Cognitively-Inspired Multi-Agent Framework for Radiology Report Generation
German Research Center for Artificial Intelligence（德国人工智能研究中心）；Intelligentx GmbH（智能X有限公司）；Department of Core Informatics, Graduate School of Informatics, Osaka Metropolitan University（大阪城市大学信息科学研究生院核心信息学系）
Whole-body CT attenuation and volume charts from routine clinical scans via evidence-grounded LLM report filtering
Institute of Radiology, School of Medicine and Health, Technical University of Munich（医学与健康学院放射学研究所，慕尼黑技术大学）；Institute of Neuroradiology, School of Medicine and Health, Technical University of Munich（医学与健康学院神经放射学研究所，慕尼黑技术大学）；Munich Data Science Institute (MDSI), Technical University of Munich（慕尼黑数据科学研究所（MDSI），慕尼黑技术大学）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心（MCML），德国慕尼黑）
Grounded Knowledge-Enhanced Medical Vision-Language Pre-training for Chest X-Ray
The Chinese University of Hong Kong（香港中文大学）；CU Lab for AI in Radiology (CLAIR), The Chinese University of Hong Kong（香港中文大学放射学人工智能实验室（CLAIR））；Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；China Unicom Global Limited（中国联通国际有限公司）
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）
We propose and demonstrate machine learning algorithms to assess the severity of pulmonary edema in chest x-ray images of congestive heart failure patients. Accurate assessment of pulmonary edema in heart failure is critical when making treatment and disposition decisions. Our work is grounded in a large-scale clinical dataset of over 300,000 x-ray images with associated radiology reports. While edema severity labels can be extracted unambiguously from a small fraction of the radiology reports, accurate annotation is challenging in most cases. To take advantage of the unlabeled images, we deve…
Scalable Training of Spatially Grounded 2D Vision-Language Models for Radiology
Computer Vision Group, University of Freiburg, Germany（德国弗莱堡大学计算机视觉组）；Department of Radiology, Medical Center -- University of Freiburg, Germany（德国弗莱堡大学医学中心放射科）；CRIION-AI Lab, Freiburg, Germany（德国弗莱堡CRIION-AI实验室）
Improving Factuality of 3D Brain MRI Report Generation with Paired Image-domain Retrieval and Text-domain Augmentation
Cancer Biology, Seoul National University College of Medicine, Korea（首尔国立大学医学院癌症生物学系，韩国）；Radiology, Massachusetts General Hospital（麻省总医院放射科）；Harvard Medical School（哈佛医学院）；Biomedical Systems Informatics, Yonsei University College of Medicine, Korea（延世大学医学院生物医学系统信息学系，韩国）；Graduate School, Yonsei University, Korea（延世大学研究生院，韩国）；Radiology, Seoul National University College of Medicine, Korea（首尔国立大学医学院放射科，韩国）；Radiology, Seoul National University Hospital, Korea（首尔国立大学医院放射科，韩国）；Radiology, Seoul National University Bundang Hospital, Korea（首尔国立大学 Bundang 医院放射科，韩国）；Radiology, SMG-SNU Boramae Medical Center, Korea（SMG-SNU Boramae 医疗中心放射科，韩国）；Psychiatry, Yonsei University College of Medicine, Korea（延世大学医学院精神病学系，韩国）；Behavioral Sciences in Medicine, Yonsei University College of Medicine, Korea（延世大学医学院医学行为科学系，韩国）；Yonsei Institute for Digital Health, Korea（延世大学数字健康研究院，韩国）；Kim Jaechul Graduate School of AI, KAIST, Korea（金 Jaechul人工智能研究生院，韩国）
ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（生物医学工程学院，生命科学与医学系，中国科学技术大学）；Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE) Lab, YRD-RIGHT, USTC Suzhou Institute for Advanced Research（医学影像、机器人、分析计算与学习（MIRACLE）实验室，YRD-RIGHT，中国科学技术大学苏州研究院）；Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology（江苏省多模态数字孪生技术重点实验室）；Biomedical Basic Research Center (BBRC) of Jiangsu Province（江苏省生物医学基础研究中心）；Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, USTC（放射科，中国科学技术大学第一附属医院，生命科学与医学系，中国科学技术大学）；Anhui IFLYTEK CO., Ltd（安徽科大讯飞股份有限公司）；School of Medicine, Stanford University（医学院，斯坦福大学）；State Key Laboratory of Precision and Intelligent Chemistry, Hefei, Anhui, China（安徽省精密与智能化学重点实验室，合肥，安徽，中国）
Multimodal Attention-Aware Fusion for Diagnosing Distal Myopathy: Evaluating Model Interpretability and Clinician Trust
Information Systems Group, Eindhoven University of Technology, The Netherlands（埃因霍温技术大学信息系统组）；Eindhoven Artificial Intelligence Systems Institute, The Netherlands（埃因霍温人工智能系统研究所）；Department of Computer Science and Technology, University of Cambridge, United Kingdom（剑桥大学计算机科学与技术系）；Department of Medicine - DIMED, Padua University Hospital, Italy（帕多瓦大学医院医学部-DIMED）；Department of Environmental Sciences, Informatics, and Statistics, Ca’ Foscari University of Venice, Italy（威尼斯卡弗里大学环境科学、信息学与统计学系）
EXACT: an explainable anomaly-aware vision foundation model for analysis of 3D chest CT
School of Biomedical Engineering, Tsinghua University（清华大学生物医学工程学院）；Department of Radiology, Mianyang Central Hospital（绵阳市中心医院放射科）；Center for Biomedical Imaging Research, Tsinghua University（清华大学生物医学成像研究中心）
Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach
School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Department of Radiology, Renmin Hospital of Wuhan University（武汉大学仁医院放射科）
CLEAR: A Clinically-Grounded Tabular Framework for Radiology Report Evaluation
University of Chicago（芝加哥大学）；Tsinghua University（清华大学）；Zhejiang University（浙江大学）；University of Michigan Department of Statistics（密歇根大学统计学系）；University of Chicago Department of Computer Science（芝加哥大学计算机科学系）；University of Chicago Department of Computer Science and Technology（芝加哥大学计算机科学与技术系）；Tsinghua University Department of Radiology（清华大学放射学系）；University of Chicago College of Control Science and Engineering（芝加哥大学控制科学与工程学院）
University of Pittsburgh（匹兹堡大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh Medical School（匹兹堡大学医学院）
We propose a BlackBox Counterfactual Explainer, designed to explain image classification models for medical applications. Classical approaches (e.g., saliency maps) that assess feature importance do not explain "how" imaging features in important anatomical regions are relevant to the classification decision. Our framework explains the decision for a target class by gradually "exaggerating" the semantic effect of the class in a query image. We adopted a Generative Adversarial Network (GAN) to generate a progressive set of perturbations to a query image, such that the classification decision ch…
ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports
Department of Biomedical Informatics, Harvard Medical School（哈佛医学院生物医学信息学系）；SSM Health, St. Louis, MO（SSM健康系统，圣路易斯，密苏里州）；Saint Louis University School of Medicine（圣路易斯大学医学院）；Icahn School of Medicine at Mount Sinai（西奈山医院伊坎医学院）；National Strategic Technology Research Institute, Seoul National University Hospital（首尔国立大学医院国家战略技术研究机构）；Brigham and Women’s Hospital（布里金和妇女医院）；Chest Radiology Division, Medical Imaging Department, King Abdullah Specialized Children’s Hospital（国王阿卜杜勒阿齐兹特殊儿童医院放射科，医学成像部门）；King Abdulaziz Medical City, Ministry of National Guard Health Affairs（国王阿卜杜勒阿齐兹医疗城，国民卫队卫生事务部）
Detection and Localization of Subdural Hematoma Using Deep Learning on Computed Tomography
Operations Research Center, Massachusetts Institute of Technology（麻省理工学院运营研究中心）；Boston University（波士顿大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Connecticut School of Medicine（康奈尔大学医学学院）；Hartford HealthCare（哈特福德医疗集团）；Sloan School of Management, Massachusetts Institute of Technology（麻省理工学院斯隆管理学院）
RadReason: Radiology Report Evaluation Metric with Reasons and Sub-Scores
School of Electrical and Computer Engineering, University of Sydney（悉尼大学电气与计算机工程学院）；School of Computer Science, University of Adelaide（阿德莱德大学计算机科学学院）；School of Computing and Information Technology, University of Wollongong（沃林根大学计算与信息科技学院）
Rethinking Patient Education as Multi-turn Multi-modal Interaction
VA Bedford Health Care（VA贝德福德医疗中心）；UMass Amherst（马萨诸塞大学阿默斯特分校）；UMass Lowell（马萨诸塞大学洛厄尔分校）；Yale University（耶鲁大学）；National University of Singapore（新加坡国立大学）；Yale School of Medicine（耶鲁医学院）
