To effectively train medical students to become qualified radiologists, a large number of X-ray images collected from patients with diverse medical conditions are needed. However, due to data privacy concerns, such images are typically difficult to obtain. To address this problem, we develop methods to generate view-consistent, high-fidelity, and high-resolution X-ray images from radiology reports to facilitate radiology training of medical students. This task is presented with several challenges. First, from a single report, images with different views (e.g., frontal, lateral) need to be generated. How to ensure consistency of these images (i.e., make sure they are about the same patient)? Second, X-ray images are required to have high resolution. Otherwise, many details of diseases would be lost. How to generate high-resolutions images? Third, radiology reports are long and have complicated structure. How to effectively understand their semantics to generate high-fidelity images that accurately reflect the contents of the reports? To address these three challenges, we propose an XRayGAN composed of three modules: (1) a view consistency network that maximizes the consistency between generated frontal-view and lateral-view images; (2) a multi-scale conditional GAN that progressively generates a cascade of images with increasing resolution; (3) a hierarchical attentional encoder that learns the latent semantics of a radiology report by capturing its hierarchical linguistic structure and various levels of clinical importance of words and sentences. Experiments on two radiology datasets demonstrate the effectiveness of our methods. To our best knowledge, this work represents the first one generating consistent and high-resolution X-ray images from radiology reports. The code is available at https://github.com/UCSD-AI4H/XRayGAN.
ICON: Improving Inter-Report Consistency in Radiology Report Generation via Lesion-aware Mixup Augmentation
The Hong Kong Polytechnic University（香港理工大学）；Southern University of Science and Technology（南方科技大学）；Research Institute of Trustworthy Autonomous Systems（可信自主系统研究院）
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）
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
Integrating MedCLIP and Cross-Modal Fusion for Automatic Radiology Report Generation
The Bishop Strachan School（毕夏普斯特拉坎学校）；Baidu Inc.（百度公司）；Beihang University（北京航空航天大学）；Institute of Future Education（未来教育研究所）；TechArena Canada Inc.（加拿大泰克Arena公司）；College of Engineering（工程学院）；Northeastern University（东北大学）
CBM-RAG: Demonstrating Enhanced Interpretability in Radiology Report Generation with Multi-Agent RAG and Concept Bottleneck Models
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Saarland University（萨尔兰大学）；University of Oldenburg（奥尔登堡大学）；Dhaka Medical College Hospital（达卡医学院医院）
The Double Dilemma in Multi-Task Radiology Report Generation: A Gradient Dynamics Analysis and Solution
School of Computer Science and Technology（计算机科学与技术学院）；Xinjiang University（新疆大学）；Information Security Engineering Technology Research Center（信息安全工程技术研究中心）
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）
HC-LLM: Historical-Constrained Large Language Models for Radiology Report Generation
Beijing University of Technology（北京工业大学）；Stanford University（斯坦福大学）；Hunan University（湖南大学）；University of Science and Technology of China（中国科学技术大学）；The University of Sydney（悉尼大学）
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）
Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification
National University of Singapore（新加坡国立大学）；Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）；Singapore University of Technology and Design（新加坡科技设计大学）
T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistency
Imperial College London（帝国理工学院）；University of Oxford（牛津大学）；Hong Kong University of Science and Technology（香港科技大学）；Peking University（北京大学）；University of Science and Technology of China（中国科学技术大学）
Prototype-Enhanced Confidence Modeling for Cross-Modal Medical Image-Report Retrieval
School of Computer Science, The University of Nottingham, NG8 1BB Nottingham, U.K.（计算机科学学院，诺丁汉大学）；Department of Intelligent Science, Xi’an Jiaotong-Liverpool University, China, 215123.（智能科学系，西安交通大学利物浦大学）
KOAL: Knowledge-Driven Prostate Cancer Grading with Ordinal-Aware Learning
School of Computer Science, Sichuan University（四川大学计算机学院）；China Mobile (Suzhou) Software Technology Company Limited（中国移动（苏州）软件技术有限公司）；Shanghai Changhai Hospital, Naval Medical University（上海长海医院，海军医学大学）；Renji Hospital, Shanghai Jiao Tong University School of Medicine（仁济医院，上海交通大学医学院）
MG-3D: Multi-Grained Knowledge-Enhanced 3D Medical Vision-Language Pre-training
The Hong Kong University of Science and Technology（香港科技大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen People’s Hospital（深圳市人民医院）；The University of Hong Kong（香港大学）；Chinese PLA General Hospital（中国人民解放军总医院）
RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports
Johns Hopkins University（约翰霍普金斯大学）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（马萨诸塞总医院）；University of California, San Francisco（加利福尼亚大学旧金山分校）；University of Zurich（苏黎世大学）；Istanbul Medipol University（伊斯坦布尔梅迪波尔大学）；Johns Hopkins Medicine（约翰霍普金斯医学）
TRACE: Temporally Reliable Anatomically-Conditioned 3D CT Generation with Enhanced Efficiency
Department of Computer Science, Durham University（杜伦大学计算机科学系）；Department of Automation, Tsinghua University（清华大学自动化系）；College of Computer Science and Engineering, Dalian Minzu University（大连民族大学计算机科学与工程学院）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Jarvis Research Center, Tencent YouTu Lab（腾讯YouTu实验室 Jarvis 研究中心）；School of Engineering Mathematics and Technology, University of Bristol（布里斯托大学工程数学与技术学院）；Medical Artificial Intelligence Laboratory, School of Engineering, Westlake University（西湖大学工程学院医学人工智能实验室）
Topicwise Separable Sentence Retrieval for Medical Report Generation
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）
