Radiology Report Generation (RRG) automates the creation of radiology reports from medical imaging, enhancing the efficiency of the reporting process. Longitudinal Radiology Report Generation (LRRG) extends RRG by incorporating the ability to compare current and prior exams, facilitating the tracking of temporal changes in clinical findings. Existing LRRG approaches only extract features from prior and current images using a visual pre-trained encoder, which are then concatenated to generate the final report. However, these methods struggle to effectively capture both spatial and temporal correlations during the feature extraction process. Consequently, the extracted features inadequately capture the information of difference across exams and thus underrepresent the expected progressions, leading to sub-optimal performance in LRRG. To address this, we develop a novel dynamic difference-aware temporal residual network (DDaTR). In DDaTR, we introduce two modules at each stage of the visual encoder to capture multi-level spatial correlations. The Dynamic Feature Alignment Module (DFAM) is designed to align prior features across modalities for the integrity of prior clinical information. Prompted by the enriched prior features, the dynamic difference-aware module (DDAM) captures favorable difference information by identifying relationships across exams. Furthermore, our DDaTR employs the dynamic residual network to unidirectionally transmit longitudinal information, effectively modelling temporal correlations. Extensive experiments demonstrated superior performance over existing methods on three benchmarks, proving its efficacy in both RRG and LRRG tasks.
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）
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
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）
Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation
School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；Xi’an Key Laboratory of Big Data and Intelligent Vision（西安大数据与智能视觉重点实验室）；Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University（教育部协同智能系统重点实验室）；Warren Alpert Medical School, Brown University（布朗大学沃伦·阿尔佩特医学学院）
RadThinking: A Dataset for Longitudinal Clinical Reasoning in Radiology
Department of Computer Science, Johns Hopkins University（约翰霍普金斯大学计算机科学系）；Clinic of Radiology and Nuclear Medicine, University Hospital Basel（巴塞尔大学医院放射科与核医学科）；Department of Oncology, Johns Hopkins School of Medicine（约翰霍普金斯医学院肿瘤科）
A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network
Children’s Hospital of Philadelphia（费城儿童医院）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；National Cancer Institute（美国国家癌症研究所）；Children's National Hospital（儿童国家医院）；Sheikh Zayed Institute for Pediatric Surgical Innovation（谢赫扎耶德儿科外科创新研究院）
FDG PET/CT imaging is a resource intensive examination critical for managing malignant disease and is particularly important for longitudinal assessment during therapy. Approaches to automate longtudinal analysis present many challenges including lack of available longitudinal datasets, managing complex large multimodal imaging examinations, and need for detailed annotations for traditional supervised machine learning. In this work we develop OncoNet, novel machine learning algorithm that assesses treatment response from a 1,954 pairs of sequential FDG PET/CT exams through weak supervision usi…
LesiOnTime -- Joint Temporal and Clinical Modeling for Small Breast Lesion Segmentation in Longitudinal DCE-MRI
Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria（医学影像研究实验室，生物医学影像与影像引导治疗系，维也纳医科大学）；Comprehensive Center for AI in Medicine, Medical University of Vienna, Austria（医学人工智能综合中心，维也纳医科大学）；Breast Health Center, Privatklinik Döbling, Austria（乳腺健康中心，多克林根私人诊所）；Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria（生物医学影像与影像引导治疗系，维也纳医科大学）；Department of Obstetrics and Gynecology, Medical University of Vienna, Austria（妇产科系，维也纳医科大学）；Department of Pathology, Medical University of Vienna, Austria（病理学系，维也纳医科大学）；Comprehensive Cancer Center, Medical University of Vienna, Austria（综合癌症中心，维也纳医科大学）
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）
Despite its tremendous value for the diagnosis, treatment monitoring and surveillance of children with cancer, whole body staging with positron emission tomography (PET) is time consuming and associated with considerable radiation exposure. 100x (1% of the standard clinical dosage) ultra-low-dose/ultra-fast whole-body PET reconstruction has the potential for cancer imaging with unprecedented speed and improved safety, but it cannot be achieved by the naive use of machine learning techniques. In this study, we utilize the global similarity between baseline and follow-up PET and magnetic resonan…
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）
Merlin Plus: A Large-Scale, Multi-Cancer, Image-Mask-Report Dataset
Johns Hopkins University（约翰斯·霍普金斯大学）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（马萨诸塞总医院）；Jagiellonian University（雅盖隆大学）；Stanford University（斯坦福大学）；Warmian-Masurian Cancer Center（瓦尔米亚-马祖里癌症中心）；University of California, San Francisco（加利福尼亚大学旧金山分校）；Johns Hopkins Medicine（约翰斯·霍普金斯医学）
Longitudinal Data and a Semantic Similarity Reward for Chest X-Ray Report Generation
The Australian e-Health Research Centre, CSIRO Health and Biosecurity（澳大利亚联邦科学与工业研究组织健康与生物安全部澳大利亚电子健康研究中心）；School of Electrical Engineering & Computer Science, University of Queensland（昆士兰大学电气工程与计算机科学学院）；Imaging Associates（影像协会）；St Vincent’s Hospital（圣文森特医院）；Monash Health（莫纳什医疗中心）
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）
3D-RAD: A Comprehensive 3D Radiology Med-VQA Dataset with Multi-Temporal Analysis and Diverse Diagnostic Tasks
ZJU-Angelalign R&D Center for Intelligence Healthcare（浙江大学智能医疗研发中心）；Zhejiang University（浙江大学）；Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence（浙江省医学影像人工智能重点实验室）；Guangdong Institute of Intelligence Science and Technology（广东省智能科学与技术研究院）
