Longitudinal NSCLC Treatment Progression via Multimodal Generative Models
Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma, Italy（人工智能与计算机系统单位，工程系，罗马大学生物医学学院）；Multi-Specialist Clinical Institute for Orthopaedic Trauma Care (COT), Messina, Italy（骨科创伤护理多学科临床研究所（COT），意大利Messina）；Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University, Sweden（诊断与介入系，放射物理，生物医学工程，乌梅大学，瑞典）；Operative Research Unit of Radiation Oncology, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy（放射肿瘤手术研究单位，大学生物医学学院基金会，罗马，意大利）；Research Unit of Radiation Oncology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Italy（放射肿瘤研究单位，医学与外科系，罗马大学生物医学学院，意大利）；Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland（生物医学工程系，巴塞尔大学，瑞士Allschwil）；Department of Naval, Electrical, Electronics and Telecommunications Engineering, University of Genoa, Italy（海军、电气、电子与电信工程系，热那亚大学，意大利）
Massimiliano Mantegna, Elena Mulero Ayllón, Alice Natalina Caragliano, Francesco Di Feola, Claudia Tacconi, Michele Fiore, Edy Ippolito, Carlo Greco, Sara Ramel… 展开作者
Massimiliano Mantegna, Elena Mulero Ayllón, Alice Natalina Caragliano, Francesco Di Feola, Claudia Tacconi, Michele Fiore, Edy Ippolito, Carlo Greco, Sara Ramella, Philippe C. Cattin, Paolo Soda, Matteo Tortora, Valerio Guarrasi
Predicting tumor evolution during radiotherapy is a clinically critical challenge, particularly when longitudinal changes are driven by both anatomy and treatment. In this work, we introduce a Virtual Treatment (VT) framework that formulates non-small cell lung cancer (NSCLC) progression as a dose-aware multimodal conditional image-to-image translation problem. Given a CT scan, baseline clinical variables, and a specified radiation dose increment, VT aims to synthesize plausible follow-up CT images reflecting treatment-induced anatomical changes. We evaluate the proposed framework on a longitudinal dataset of 222 stage III NSCLC patients, comprising 895 CT scans acquired during radiotherapy under irregular clinical schedules. The generative process is conditioned on delivered dose increments together with demographic and tumor-related clinical variables. Representative GAN-based and diffusion-based models are benchmarked across 2D and 2.5D configurations. Quantitative and qualitative results indicate that diffusion-based models benefit more consistently from multimodal, dose-aware conditioning and produce more stable and anatomically plausible tumor evolution trajectories than GAN-based baselines, supporting the potential of VT as a tool for in-silico treatment monitoring and adaptive radiotherapy research in NSCLC.
4DLoG: Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model
Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Radiology（放射学系）；Department of Electrical and Computer Engineering, Department of Computer Science（电气与计算机工程系、计算机科学系）
Learning Patient-Specific Disease Dynamics with Latent Flow Matching for Longitudinal Imaging Generation
University of Cambridge（剑桥大学）；Nanjing First Hospital（南京第一医院）；Nanjing Medical University（南京医科大学）；Johns Hopkins University（约翰霍普金斯大学）；University of Dundee（邓迪大学）
Align-cDAE: Alzheimer's Disease Progression Modeling with Attention-Aligned Conditional Diffusion Auto-Encoder
Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India（电子工程系，印度理工学院马德拉斯，钦奈，印度）；Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, India（苏达·戈帕拉克里希南脑中心，印度理工学院马德拉斯，钦奈，印度）；Department of Electrical Engineering, Indian Institute of Technology Madras Chennai, India（电子工程系，印度理工学院马德拉斯钦奈，印度）
University of Copenhagen（哥本哈根大学）；Rigshospitalet（哥本哈根大学国立医院）
Medical imaging plays a vital role in modern diagnostics and treatment. The temporal nature of disease or treatment progression often results in longitudinal data. Due to the cost and potential harm, acquiring large medical datasets necessary for deep learning can be difficult. Medical image synthesis could help mitigate this problem. However, until now, the availability of GANs capable of synthesizing longitudinal volumetric data has been limited. To address this, we use the recent advances in latent space-based image editing to propose a novel joint learning scheme to explicitly embed tempor…
Medical Video Generation for Disease Progression Simulation
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；SambaNova System, Inc（SambaNova系统公司）；Objective, Inc（Objective公司）；Microsoft（微软公司）；University of Virginia（弗吉尼亚大学）；Shenzhen Nanshan People’s Hospital（深圳市南山区人民医院）；Shenzhen Children’s Hospital（深圳市儿童医院）
Alzheimers Disease Progression Prediction Based on Manifold Mapping of Irregularly Sampled Longitudinal Data
College of Computer Science and Technology, Huaqiao University（华侨大学计算机科学与技术学院）；Key Laboratory of Computer Vision and Machine Learning in Fujian Province（福建省计算机视觉与机器学习重点实验室）；College of Information Science and Engineering,Ritsumeikan University（立命馆大学信息科学与工程学院）
Kyung Hee University（庆熙大学）；Sharif University of Technology（谢里夫理工大学）；Technical University of Munich（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation. Image segmentation methods have proven to help quantify the disease burden and even help predict the outcome. The availability of longitudinal CT series may also result in an efficient and effective method to reliably assess the progression of COVID-19, monitor the healing process and the response to different therapeutic strategies. In this paper, we propose a new framework to identify infection at…
4D VQ-GAN: Synthesising Medical Scans at Any Time Point for Personalised Disease Progression Modelling of Idiopathic Pulmonary Fibrosis
Hawkes Institute, University College London（伦敦大学学院霍克斯研究所）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）；Department of Med. Physics & Biomedical Engineering, University College London（伦敦大学学院医学物理与生物医学工程系）；Department of Respiratory Medicine, University Hospitals Leuven（鲁汶大学医院呼吸医学科）；NIHR Southampton Biomedical Research Centre and Clinical and Experimental Sciences, University of Southampton（南安普顿大学NIHR南安普顿生物医学研究中心与临床与实验科学系）；UCL Respiratory, University College London（伦敦大学学院呼吸科）
How Good are Foundation Models in Longitudinal MRI Disease Progression Reasoning?
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Department of Health Abu Dhabi（阿布扎比卫生部）；Fatima College of Health Sciences（法蒂玛健康科学学院）；Linköping University（林雪平大学）
GLOMIA-Pro: A Generalizable Longitudinal Medical Image Analysis Framework for Disease Progression Prediction
School of Medical Technology, Beijing Institute of Technology（北京理工大学医学技术学院）；Department of Thoracic Surgery, Sun Yat-sen University Cancer Center（中山大学肿瘤医院胸外科部门）；Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University（中山大学第五附属医院放射科部门）；Beijing Duanliu Medical Technology Co., Ltd.（北京杜流医疗科技有限公司）
Medical University of Vienna（维也纳医科大学）；Centre for Eye Research Australia（澳大利亚眼科研究中心）；Royal Victorian Eye and Ear Hospital（皇家维多利亚眼耳医院）；The University of Melbourne（墨尔本大学）
Longitudinal imaging is capable of capturing the static ana\-to\-mi\-cal structures and the dynamic changes of the morphology resulting from aging or disease progression. Self-supervised learning allows to learn new representation from available large unlabelled data without any expert knowledge. We propose a deep learning self-supervised approach to model disease progression from longitudinal retinal optical coherence tomography (OCT). Our self-supervised model takes benefit from a generic time-related task, by learning to estimate the time interval between pairs of scans acquired from the sa…
Self-supervised Pre-training Helps Retinal Disease Progression Modelling Most When Data Is Scarce
Hertie Institute for AI in Brain Health, Faculty of Medicine, University of Tübingen（赫蒂人工智能脑健康研究所，图宾根大学医学院）；Tübingen AI Center, University of Tübingen（图宾根人工智能中心，图宾根大学）
Stanford University（斯坦福大学）；Stanford School of Medicine（斯坦福医学院）；University of California San Diego（加州大学圣地亚哥分校）；SRI International（斯坦福国际研究院）
Many neurological diseases are characterized by gradual deterioration of brain structure and function. Large longitudinal MRI datasets have revealed such deterioration, in part, by applying machine and deep learning to predict diagnosis. A popular approach is to apply Convolutional Neural Networks (CNN) to extract informative features from each visit of the longitudinal MRI and then use those features to classify each visit via Recurrent Neural Networks (RNNs). Such modeling neglects the progressive nature of the disease, which may result in clinically implausible classifications across visits…
University of Iowa（爱荷华大学）；Stanford University（斯坦福大学）
Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography-computed tomography (PET/CT) images have predictive power on NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neu…
L-MAE: Longitudinal masked auto-encoder with time and severity-aware encoding for diabetic retinopathy progression prediction
LaTIM UMR 1101, Inserm（法国国家健康与医学研究院UMR 1101拉蒂姆研究所）；University of Western Brittany（西布列塔尼大学）；IMT Atlantique（大西洋电信学院）；Lariboisière Hospital, AP-HP（巴黎公立医院集团拉里布瓦西耶尔医院）；University Hospital of Brest（布雷斯特大学医院）；INSERM U1227 Lymphocytes B et Autoimmunite (LBAI)（法国国家健康与医学研究院UMR 1227 B淋巴细胞与自身免疫研究所）
3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs
Medical University of Vienna（维也纳医科大学）；BioMedIA, Imperial College London（伦敦帝国理工学院生物医学图像分析研究所）；Institute for AI and Informatics in Medicine, Klinikum rechts der Isar, Technical University Munich（慕尼黑工业大学伊萨尔河畔医院医学人工智能与信息学研究所）；Department of Ophthalmology, University of Basel（巴塞尔大学眼科系）
Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）
Multi-modal biological, imaging, and neuropsychological markers have demonstrated promising performance for distinguishing Alzheimer's disease (AD) patients from cognitively normal elders. However, it remains difficult to early predict when and which mild cognitive impairment (MCI) individuals will convert to AD dementia. Informed by pattern classification studies which have demonstrated that pattern classifiers built on longitudinal data could achieve better classification performance than those built on cross-sectional data, we develop a deep learning model based on recurrent neural networks…
ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Images
Yale University（耶鲁大学）；University of California, San Francisco（加州大学旧金山分校）；Mila - Quebec AI Institute（魁北克AI研究所）；Université de Montréal（蒙特利尔大学）；McGill University（麦吉尔大学）；Northern California Retina Vitreous Associates（北加州视网膜与玻璃体协会）
We propose a method to predict the subject-specific longitudinal progression of brain structures extracted from baseline MRI, and evaluate its performance on Alzheimer's disease data. The disease progression is modeled as a trajectory on a group of diffeomorphisms in the context of large deformation diffeomorphic metric mapping (LDDMM). We first exhibit the limited predictive abilities of geodesic regression extrapolation on this group. Building on the recent concept of parallel curves in shape manifolds, we then introduce a second predictive protocol which personalizes previously learned traj…
Inverse Bayesian Inference for Extracting Lesion Dynamics from Longitudinal Spectral CT
University Hospital Augsburg（奥格斯堡大学医院）；Technical University of Munich（慕尼黑工业大学）；Bavarian Center for Cancer Research (BZKF)（巴伐利亚癌症研究中心）；University of Augsburg（奥格斯堡大学）
Lab for Artificial Intelligence in Medical Imaging (AI-Med), KJP, LMU Klinikum（慕尼黑大学附属医院KJP医学影像人工智能实验室（AI-Med））；Technical University of Munich, School of Medicine（慕尼黑工业大学医学院）
Modeling temporal changes in subcortical structures is crucial for a better understanding of the progression of Alzheimer's disease (AD). Given their flexibility to adapt to heterogeneous sequence lengths, mesh-based transformer architectures have been proposed in the past for predicting hippocampus deformations across time. However, one of the main limitations of transformers is the large amount of trainable parameters, which makes the application on small datasets very challenging. In addition, current methods do not include relevant non-image information that can help to identify AD-related…
Robust forecasting of the future anatomical changes inflicted by an ongoing disease is an extremely challenging task that is out of grasp even for experienced healthcare professionals. Such a capability, however, is of great importance since it can improve patient management by providing information on the speed of disease progression already at the admission stage, or it can enrich the clinical trials with fast progressors and avoid the need for control arms by the means of digital twins. In this work, we develop a deep learning method that models the evolution of age-related disease by proce…
Diagnostic-Guided Longitudinal Modeling for Forecasting Retinal Atrophy Progression
Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School（眼科部门，麻省眼耳医院，哈佛医学院）；Harvard Ophthalmology AI Lab, Schepens Eye Research Institute, Harvard Medical School（哈佛眼科人工智能实验室，斯科普斯眼研究所，哈佛医学院）；Ocular Genomics Institute, Massachusetts Eye and Ear, Harvard Medical School（眼科基因组研究所，麻省眼耳医院，哈佛医学院）；Broad Institute of MIT and Harvard, Cambridge, MA, USA（麻省理工学院与哈佛大学 Broad 实验室，剑桥，马萨诸塞州，美国）
Detection of diabetic retinopathy using longitudinal self-supervised learning
LaTIM UMR 1101, Inserm（LaTIM UMR 1101 法国国家健康与医学研究院）；University of Western Brittany（西布列塔尼大学）；IMT Atlantique（大西洋电信学院）；Lariboisière Hospital, AP-HP（拉里布瓦西耶医院（巴黎公共医疗集团））；CHRU Brest（布雷斯特大学医疗中心）
Brain Latent Progression: Individual-based Spatiotemporal Disease Progression on 3D Brain MRIs via Latent Diffusion
Dept. of Math and Computer Science, University of Catania（数学与计算机科学系，卡塔尼亚大学）；Centre for Medical Image Computing, University College London（医学影像计算中心，伦敦大学学院）；MIFT Department, University of Messina（MIFT系，梅斯ina大学）
提出 BrLP 时空模型，通过在小型潜在空间运行、整合受试者元数据与疾病动力学先验，并引入 LAS 算法，实现了 3D 脑部 MRI 个体化疾病进展的高精度预测。
Multi-View Imputation and Cross-Attention Network Based on Incomplete Longitudinal and Multimodal Data for Conversion Prediction of Mild Cognitive Impairment
Progression as Latent Drift: Generative Forecasting of Slow-Evolving Pathologies
Zhejiang University（浙江大学）；The Hong Kong Polytechnic University（香港理工大学）；IROOTECH TECHNOLOGY（IROOTECH技术）；Wolf 1069 b Lab, Sany Group（Wolf 1069 b实验室，三一集团）
Longitudinal Self-supervised Learning Using Neural Ordinary Differential Equation
Inserm（法国国家健康与医学研究院）；University of Western Brittany（西布列塔尼大学）；IMT Atlantique（大西洋国立高等工业技术与矿业学校）；Lariboisière Hospital, AP-HP（AP-HP拉里博瓦西耶医院）；CHRU Brest（布雷斯特大学中心医院）
Forecasting Disease Progression with Parallel Hyperplanes in Longitudinal Retinal OCT
Medical University of Vienna（维也纳医科大学）；University of Basel（巴塞尔大学）；University of Michigan（密歇根大学）；NIHR Moorfields Biomedical Research Centre（NIHR穆尔菲尔德生物医学研究中心）；Technical University Munich（慕尼黑工业大学）；University of Southampton（南安普顿大学）
Given a gallery of face images of missing children, state-of-the-art face recognition systems fall short in identifying a child (probe) recovered at a later age. We propose a feature aging module that can age-progress deep face features output by a face matcher. In addition, the feature aging module guides age-progression in the image space such that synthesized aged faces can be utilized to enhance longitudinal face recognition performance of any face matcher without requiring any explicit training. For time lapses larger than 10 years (the missing child is found after 10 or more years), the …
The University of Sydney（悉尼大学）；Royal Prince Alfred Hospital（皇家阿尔弗雷德王子医院）；Harvard Medical School（哈佛医学院）
We present a longitudinal MR simulation framework which simulates the future neurodegenerative progression by outputting the predicted follow-up MR image and the voxel-based morphometry (VBM) map. This framework expects the patients to have at least 2 historical MR images available. The longitudinal and cross-sectional VBM maps are extracted to measure the affinity between the target subject and the template subjects collected for simulation. Then the follow-up simulation is performed by resampling the latest available target MR image with a weighted sum of non-linear transformations derived f…
Centre for Medical Image Computing（医学图像计算中心）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）
Simulating images representative of neurodegenerative diseases is important for predicting patient outcomes and for validation of computational models of disease progression. This capability is valuable for secondary prevention clinical trials where outcomes and screening criteria involve neuroimaging. Traditional computational methods are limited by imposing a parametric model for atrophy and are extremely resource-demanding. Recent advances in deep learning have yielded data-driven models for longitudinal studies (e.g., face ageing) that are capable of generating synthetic images in real-tim…
Morph-SSL: Self-Supervision with Longitudinal Morphing to Predict AMD Progression from OCT
Medical University of Vienna（维也纳医科大学）；Imperial College London（伦敦帝国学院）；Technical University Munich（慕尼黑工业大学）；Moorfields Eye Hospital NHS Foundation Trust（摩尔菲尔兹眼科医院NHS基金会信托）；University of Basel（巴塞尔大学）；University of Southampton（南安普顿大学）
Deep Learning-Based Longitudinal Prediction of Childhood Myopia Progression Using Fundus Image Sequences and Baseline Refraction Data
Beijing Tongren Hospital, Capital Medical University（首都医科大学附属北京同仁医院）；School of Instrumentation and Optoelectronic Engineering, Beihang University（北京航空航天大学仪器科学与光电工程学院）；School of Mechanical Engineering, Beihang University（北京航空航天大学机械工程学院）；University of Cambridge（剑桥大学）；University College London（伦敦大学学院）；The Chinese University of Hong Kong（香港中文大学）；Thorough Future Inc.（透景未来科技有限公司）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
TumorFlow: Physics-Guided Longitudinal MRI Synthesis of Glioblastoma Growth
Technical University of Munich (TUM), Germany（慕尼黑技术大学（TUM））；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；AI for Image-Guided Diagnosis and Therapy（图像引导诊断与治疗的AI）；Chair for AI in Healthcare and Medicine, TUM and TUM University Hospital, Munich, Germany（医疗与医学中的AI主任，慕尼黑技术大学及慕尼黑大学医院）
Registration of Longitudinal Spine CTs for Monitoring Lesion Growth
Harvard Medical School（哈佛医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Brigham and Women’s Hospital（布莱根妇女医院）；Isomics Inc（伊索米克斯公司）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）
Simultaneous column-based deep learning progression analysis of atrophy associated with AMD in longitudinal OCT studies
School of Computer Science and Engineering, The Hebrew University of Jerusalem（耶路撒冷希伯来大学计算机科学与工程学院）；The Alexander Grass Center for Bioengineering, The Hebrew University of Jerusalem（耶路撒冷希伯来大学亚历山大·格拉斯生物工程中心）；Hadassah Medical Center（哈达萨医疗中心）
Modelling Geographic Atrophy Progression using Implicit Neural Representations
University of Basel（巴塞尔大学）；University Hospital Basel（巴塞尔大学医院）；Moorfields Eye Hospital NHS Foundation Trust（摩尔菲尔兹眼科医院NHS基金会信托）；Vienna University of Natural Resources and Life Sciences（维也纳自然资源与生命科学大学）
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）
Recent contrastive learning methods achieved state-of-the-art in low label regimes. However, the training requires large batch sizes and heavy augmentations to create multiple views of an image. With non-contrastive methods, the negatives are implicitly incorporated in the loss, allowing different images and modalities as pairs. Although the meta-information (i.e., age, sex) in medical imaging is abundant, the annotations are noisy and prone to class imbalance. In this work, we exploited already existing temporal information (different visits from a patient) in a longitudinal optical coherence…
Stochastic Siamese MAE Pretraining for Longitudinal Medical Images
Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna（人工智能研究所，医学数据科学中心，维也纳医科大学）；Department of Ophthalmology and Optometry, Medical University of Vienna（眼科学与视光学系，维也纳医科大学）；Ophthalmic Image Analysis Group (OPTIMA), Medical University of Vienna（眼科影像分析组（OPTIMA），维也纳医科大学）；BioMedIA, Department of Computing, Imperial College London（BioMedIA，计算系，伦敦帝国理工学院）；Chair for AI in Healthcare and Medicine, Technical University of Munich（医学与健康人工智能教授职位，慕尼黑技术大学）；Moorfields National Institute for Health and Care Biomedical Research Centre, Moorfields Eye Hospital（莫尔菲尔兹国家健康与护理生物医学研究中心，莫尔菲尔兹眼科医院）
