BTReport: A Framework for Brain Tumor Radiology Report Generation with Clinically Relevant Features
University of Washington（华盛顿大学）；University of Washington School of Medicine（华盛顿大学医学院）；Microsoft Health AI（微软健康人工智能）；Johns Hopkins School of Medicine（约翰霍普金斯医学院）
Juampablo E. Heras Rivera, Dickson T. Chen, Tianyi Ren, Daniel K. Low, Asma Ben Abacha, Alberto Santamaria-Pang, Mehmet Kurt
备注：
Accepted to Medical Imaging with Deep Learning (MIDL) 2026
Recent advances in radiology report generation (RRG) have been driven by large paired image-text datasets; however, progress in neuro-oncology has been limited due to a lack of open paired image-report datasets. Here, we introduce BTReport, an open-source framework for brain tumor RRG that constructs natural language radiology reports using deterministically extracted imaging features. Unlike existing approaches that rely on large general-purpose or fine-tuned vision-language models for both image interpretation and report composition, BTReport performs deterministic feature extraction for image analysis and uses large language models only for syntactic structuring and narrative formatting. By separating RRG into a deterministic feature extraction step and a report generation step, the generated reports are completely interpretable and less prone to hallucinations. We show that the features used for report generation are predictive of key clinical outcomes, including survival and IDH mutation status, and reports generated by BTReport are more closely aligned with reference clinical reports than existing baselines for RRG. Finally, we introduce BTReport-BraTS, a companion dataset that augments BraTS imaging with synthetically generated radiology reports produced with BTReport. Code for this project can be found at https://github.com/KurtLabUW/BTReport.
S-RRG-Bench: Structured Radiology Report Generation with Fine-Grained Evaluation Framework
University of Sydney（悉尼大学）；University of Wollongong（沃林戈大学）；University of Adelaide（阿德莱德大学）；First Clinical Medical College, Guangzhou University of Chinese Medicine（广州中医药大学第一临床学院）
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）
MedPAO-VL: Interpretable and Protocol-Compliant Agent for Structured Radiology Report Generation
Indian Institute of Technology Madras（印度马德拉斯理工学院）；Indian Institute of Science（印度科学学院）；Ramaiah Medical College and Memorial hospital（拉迈亚医学院及纪念医院）；TANUH – The AI Centre of Excellence in Healthcare, Indian Institute of Science（印度科学学院TANUH医疗人工智能卓越中心）
A Review of Longitudinal Radiology Report Generation: Dataset Composition, Methods, and Performance Evaluation
School of Electrical and Computer Engineering, The University of Sydney（电气与计算机工程学院，悉尼大学）；School of Computer Science, The University of Adelaide（计算机科学学院，阿德莱德大学）；School of Computing and Information Technology, University of Wollongong（计算与信息科技学院，沃伦冈大学）
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）
Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation
Microsoft Research（微软研究院）；University of Washington（华盛顿大学）；Stanford University（斯坦福大学）；University of Southern California（南加州大学）；University of California, Davis（加利福尼亚大学戴维斯分校）
DRRG: A Discrete Diffusion Framework for Radiology Report Generation
School of Electrical and Computer Engineering, The University of Sydney（悉尼大学电气与计算机工程学院）；Department of Radiology, The First Affiliated Hospital of Soochow University（苏州大学附属第一医院放射科）；School of Computer Science, The University of Adelaide（阿德莱德大学计算机学院）；School of Computing and Information Technology, University of Wollongong（卧龙岗大学计算与信息技术学院）
Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database
National Institutes of Health Clinical Center（美国国立卫生研究院临床中心）；NVIDIA（英伟达）
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
Visual Prompt Engineering for Vision Language Models in Radiology
Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany（德国癌症研究中心医学图像计算部）；Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany（海德堡大学数学与计算机科学学院）；Medical Faculty Heidelberg, University of Heidelberg, Heidelberg, Germany（海德堡大学医学学院）
Semantically Informed Salient Regions Guided Radiology Report Generation
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（人工智能学院，北京邮电大学）；Department of Radiology, Peking University Third Hospital（北京大学第三医院放射科）
Concept-Enhanced Multimodal RAG: Towards Interpretable and Accurate Radiology Report Generation
Department of Engineering（工程系）；Research Unit of Artificial Intelligence and Computer Systems（人工智能与计算机系统研究单位）；Università Campus Bio-Medico of Roma（罗马大学生物医学校园）；Department of Computer, Control and Management Engineering（计算机、控制与管理工程系）；Sapienza University of Rome（罗马萨皮恩扎大学）；Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering（诊断与介入、辐射物理、生物医学工程系）；Umeå University（乌梅拉大学）；UniCamillus-Saint Camillus International University of Health Sciences（UniCamillus-圣卡米卢斯国际健康科学大学）
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）
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）
A Vision-language Framework for Comparative Reasoning in Radiology
University of Science and Technology of China（中国科学技术大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）；Department of Biomedical Informatics, Harvard Medical School（哈佛医学院生物医学信息学系）；Department of Radiology, Renmin Hospital of Wuhan University（武汉大学仁民医院放射科）；Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University（上海交通大学附属第六人民医院）
M3-Score: Fidelity, Memorization and Coverage as Separate Axes for Evaluating Generative Radiology Image Models
Faculty of Engineering, University of Peradeniya（佩拉德尼亚大学工程学院）；School of Computing Technologies, RMIT University（皇家墨尔本理工大学计算技术学院）；School of Engineering & Digital Technologies, University of Southern Queensland（南昆士兰大学工程与数字技术学院）；School of Engineering & Technologies, UNSW（新南威尔士大学工程与技术学院）
Curia: A Multi-Modal Foundation Model for Radiology
Raidium；Hôpital Européen Georges Pompidou, AP-HP（欧洲乔治·蓬皮杜医院, AP-HP）；Université Paris-Cité（巴黎城市大学）；INRIA（法国国家信息与自动化技术研究所）；INSERM（法国国家卫生与医学研究中心）；Beaujon Hospital（贝琼医院）；Medical University of South Carolina（南卡罗来纳医科大学）；Columbia University Irving Medical Center（哥伦比亚大学伊万斯医学中心）；Centre Cardiologique du Nord（北心脑血管中心）
Curia-2: Scaling Self-Supervised Learning for Radiology Foundation Models
Raidium；Department of Vascular and Oncological Interventional Radiology, Hôpital Européen Georges Pompidou, AP-HP, Paris, France（巴黎欧洲乔治·蓬皮杜医院血管与肿瘤介入放射科，AP-HP）；Faculté de Santé, Université Paris-Cité, Paris, France（巴黎西岱大学健康学院）
LUMEN: Longitudinal Multi-Modal Radiology Model for Prognosis and Diagnosis
1 Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington DC, USA 2 Nvidia Corporation, Santa Clara, CA, USA 3 ETSI Telecomunicaci\' o n, Universidad Polit\' e cnica de Madrid, Madrid, Spain 4 School of Medicine；Health Sciences, George Washington University, Washington DC, USA
