Vision-Language Models for Automated 3D PET/CT Report Generation
Institute of Medical Technology and National Biomedical Imaging Center, Peking University（北京大学医学技术研究院和国家生物医学成像中心）；Peking University People’s Hospital（北京大学人民医院）；Peking University Third Hospital（北京大学第三医院）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；The Affiliated Hospital of Qingdao University（青岛大学附属医院）；The First Affiliated Hospital of Henan Medical University（河南医科大学第一附属医院）；Jiujiang City Key Laboratory of Cell Therapy, Jiu Jiang NO.1 People’s Hospital（九江市细胞治疗重点实验室，九江市第一人民医院）
Wenpei Jiao, Kun Shang, Hui Li, Ke Yan, Jiajin Zhang, Guangjie Yang, Lijuan Guo, Yan Wan, Xing Yang, Dakai Jin, Zhaoheng Xie
Positron emission tomography/computed tomography (PET/CT) is essential in oncology, yet the rapid expansion of scanners has outpaced the availability of trained specialists, making automated PET/CT report generation (PETRG) increasingly important for reducing clinical workload. Compared with structural imaging (e.g., X-ray, CT, and MRI), functional PET poses distinct challenges: metabolic patterns vary with tracer physiology, and whole-body 3D contextual information is required rather than local-region interpretation. To advance PETRG, we propose PETRG-3D, an end-to-end 3D dual-branch framework that separately encodes PET and CT volumes and incorporates style-adaptive prompts to mitigate inter-hospital variability in reporting practices. We construct PETRG-Lym, a multi-center lymphoma dataset collected from four hospitals (824 reports w/ 245,509 paired PET/CT slices), and construct AutoPET-RG-Lym, a publicly accessible PETRG benchmark derived from open imaging data but equipped with new expert-written, clinically validated reports (135 cases). To assess clinical utility, we introduce PETRG-Score, a lymphoma-specific evaluation protocol that jointly measures metabolic and structural findings across curated anatomical regions. Experiments show that PETRG-3D substantially outperforms existing methods on both natural language metrics (e.g., +31.49\% ROUGE-L) and clinical efficacy metrics (e.g., +8.18\% PET-All), highlighting the benefits of volumetric dual-modality modeling and style-aware prompting. Overall, this work establishes a foundation for future PET/CT-specific models emphasizing disease-aware reasoning and clinically reliable evaluation. Codes, models, and AutoPET-RG-Lym will be released.
PETAR: Localized Findings Generation with Mask-Aware Vision-Language Modeling for PET Automated Reporting
University of Wisconsin–Madison Department of Computer Sciences（威斯康星大学麦迪逊分校计算机科学系）；University of Wisconsin–Madison Department Radiology（威斯康星大学麦迪逊分校放射学系）；Microsoft（微软公司）
MetaStructAtlas: A Grounded 3D Vision-Language Dataset and Benchmark for Functional and Structural Reasoning in Whole-Body PET/CT
Shanghai Academy of Artificial Intelligence for Science（上海科学人工智能研究院）；Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化研究院）；Huashan Hospital（华山医院）；Human Phenome Institute, Fudan University（复旦大学人类表型组研究院）；Shanghai Universal Medical Imaging Diagnostic Center（上海全景医学影像诊断中心）
A Unified Vision-Language Model for PSMA PET/CT Report Generation, Visual Question Answering, and Lesion Segmentation
University of Florida（佛罗里达大学）；University of California, San Francisco（加利福尼亚大学旧金山分校）；Stony Brook University（纽约州立大学石溪分校）；The University of Texas MD Anderson Cancer Center（得克萨斯大学MD安德森癌症中心）
PETR: Prompt Ensembling with Training-free Routing for Vision-Language Models
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Purple Mountain Laboratories（紫金山实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）
M2IST: Multi-Modal Interactive Side-Tuning for Efficient Referring Expression Comprehension
College of Electronics and Information Engineering, Sichuan University（四川大学电子信息学院）；College of Systems Engineering, National University of Defense Technology（国防科技大学系统工程学院）；State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Engineering, Westlake University（西湖大学工程学院）；College of Computer Science and Cyber Security (Pilot Software College), Chengdu University of Technology（成都理工大学计算机与网络安全学院（软件学院））；Yunnan Key Laboratory of Software Engineering, Yunnan University（云南大学云南省软件工程重点实验室）
Automated Report-Derived Oncology VQA Benchmark for Evaluating Vision-Language Models on 3D Medical Imaging
UCSF–UC Berkeley Joint Graduate Program in Bioengineering（UCSF-伯克利生物工程联合研究生项目）；Department of Radiology, UCSF（UCSF放射科）；Department of Radiation Oncology, UCSF（UCSF放射肿瘤科）
GeoVLM: Improving Automated Vehicle Geolocalisation Using Vision-Language Matching
Connected and Autonomous Vehicles Lab (CAV-Lab)（连接与自动驾驶车辆实验室）；University of Surrey（萨里大学）；Centre for Vision, Speech and Signal Processing (CVSSP)（视觉、语音和信号处理中心）
Automated Lesion Segmentation in Whole-Body PET/CT in a multitracer setting
Shanghai United Imaging Healthcare Advanced Technology Research Institute Co., Ltd.（上海联影医疗科技股份有限公司先进技术研究院）；Shanghai Jiao Tong University（上海交通大学）；East China Normal University（华东师范大学）
AutoLugano: A Deep Learning Framework for Fully Automated Lymphoma Segmentation and Lugano Staging on FDG-PET/CT
East China Normal University（华东师范大学）；Changhai Hospital, Naval Medical University（海军军医大学长海医院）；Shanghai Fourth People’s Hospital Affiliated to Tongji University, School of Medicine（同济大学医学院附属上海市第四人民医院）；RadioDynamic Medical（瑞迪动力医疗）；Tsinghua Cross-Strait Research Institute（清华海峡研究院）
Toward a Vision-Language Foundation Model for Medical Data: Multimodal Dataset and Benchmarks for Vietnamese PET/CT Report Generation
AI4LIFE, Hanoi University of Science and Technology, Vietnam（AI4LIFE，河内科学技术大学，越南）；Nagoya University, Japan（名古屋大学，日本）；AIST, Japan（日本国家先进工业技术研究院）；VinUniversity, Vietnam（文园大学，越南）；NVIDIA, USA（NVIDIA，美国）；Griffith University, Australia（格里菲斯大学，澳大利亚）；Hanoi Medical University, Vietnam（河内医学院，越南）；Military Central Hospital, Vietnam（越南108中央军医院）
How to Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-angle Maximum Intensity Projections and Diffusion Models
BC Cancer Research Institute（BC癌症研究所）；University of British Columbia（不列颠哥伦比亚大学）；BC Cancer（BC癌症中心）
VL-PET: Vision-and-Language Parameter-Efficient Tuning via Granularity Control
The Chinese University of Hong Kong（香港中文大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Centre for Perceptual and Interactive Intelligence（感知与交互智能中心）
Advancing from Automated to Autonomous Beamline by Leveraging Computer Vision
Department of Electrical and Computer Engineering, Cleveland State University（电气与计算机工程系，克利夫兰州立大学）；Computing and Data Sciences, Brookhaven National Laboratory（计算与数据科学部，布鲁赫斯旺国家实验室）；National Synchrotron Light Source II, Brookhaven National Laboratory（国家同步辐射光源II，布鲁赫斯旺国家实验室）
A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset
University of British Columbia（不列颠哥伦比亚大学）；BC Cancer Research Institute（不列颠哥伦比亚癌症研究所）；Microsoft（微软公司）；Seoul St. Mary’s Hospital（首尔圣玛丽医院）；The Catholic University of Korea（韩国天主教大学）；BC Cancer（不列颠哥伦比亚癌症中心）
A Computer Vision Pipeline for Automated Determination of Cardiac Structure and Function and Detection of Disease by Two-Dimensional Echocardiography
Cardiovascular Research Institute, University of California, San Francisco（加州大学旧金山分校心血管研究所）；Department of Electrical Engineering and Computer Science, University of California at Berkeley（加州大学伯克利分校电气工程与计算机科学系）；Department of Medicine, University of California, San Francisco（加州大学旧金山分校医学系）；Division of Cardiology, Department of Medicine, and Feinberg Cardiovascular Research Institute（医学系心脏病学科及范伯格心血管研究所）
The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT $\unicode{x2013}$ Multitracer Multicenter Generalization
Department of Radiology, LMU University Hospital, LMU Munich（莱比锡大学医院放射科，莱比锡大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；University Hospital Tübingen, Department of Radiology（图宾根大学医院放射科）；Department of Radiology, Stanford University（斯坦福大学放射科）；German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital（海德堡大学医院放射肿瘤学部模式分析与学习组）；Faculty of Mathematics and Computer Science, Heidelberg University（海德堡大学数学与计算机科学学院）；Institute for AI in Medicine (IKIM), University Hospital Essen (AöR)（医学人工智能研究所（IKIM），埃森大学医院（AöR））；Department of Nuclear Medicine, University Hospital Essen (AöR)（核医学部，埃森大学医院（AöR））；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程系）；Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；HIDSS4Health - Helmholtz Information and Data Science School for Health（HIDSS4Health - 海德堡信息与数据科学健康学校）；Department of Nuclear Medicine, LMU University Hospital, LMU Munich（莱比锡大学医院核医学部，莱比锡大学）；Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung Research (DZL)（综合肺科中心（CPC-M），德国肺癌研究中心（DZL）成员）；relAI – Konrad Zuse School of Excellence in Reliable AI（relAI - 卡诺德·祖斯可靠性人工智能卓越学校）；Cluster of Excellence iFIT (EXC 2180) "Image Guided and Functionally Instructed Tumor Therapies", University of Tübingen（卓越中心iFIT（EXC 2180）"图像引导和功能指导肿瘤治疗"，图宾根大学）
