Longitudinal Imaging

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3 papers in the last four weeks, down 40% on the four weeks before. 0.0% of all new papers.

Jul 6Week of Sep 21

Latest papers 27

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RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports
Sep 28, 2026Pedro R. A. S. Bassi, Wenxuan Li, Hanxue Gu +11Tumor Segmentation Longitudinal Imaging
Johns Hopkins University · Harvard Medical School · Massachusetts General Hospital +4
Clinical Trajectory Alignment for Medical Vision-Language Pre-training
Sep 28, 2026Huimin Yan, Xian Yang, Zhi Wang +1Medical Vision-Language Models Longitudinal Imaging
Institute of Intelligent Information Processing, Shanxi University, Taiyuan, China · Alliance Manchester Business School, The University of Manchester, Manchester, UK
Self-supervised Pre-training Helps Retinal Disease Progression Modelling Most When Data Is Scarce
Sep 14, 2026Ifeoma Veronica Nwabufo, Julius Gervelmeyer, Sarah Müller +1Self-Supervised Learning Longitudinal Imaging
Hertie Institute for AI in Brain Health, Faculty of Medicine, University of Tübingen, Germany · Tübingen AI Center, University of Tübingen, Germany
Brain-PACE: A Deep Siamese MRI Framework for Modelling Longitudinal Brain Acceleration
Sep 11, 2026Samuel Maddox (School of Computing Sciences, University of East Anglia), Jacob Newman (School of Computing Sciences +8Brain Age Multimodal Neuroimaging
School of Computing Sciences, University of East Anglia Norwich Research Park, Norwich, NR4 7TJ, UK · Norwich Medical School, University of East Anglia Norwich Research Park, Norwich, NR4 7TJ, UK
Robust retinal biometrics for patient identity verification and retrieval across age and imaging devices
Aug 31, 2026Jose D. Vargas-Quiros, Dennis Bontempi, Jeroen Vermeulen +3Retinal Imaging Biometrics
Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, the Netherlands · Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands · Dept. of Computational Biology, University of Lausanne, Lausanne, Switzerland +4
How Good are Foundation Models in Longitudinal MRI Disease Progression Reasoning?
Aug 13, 2026Wafa Al Ghallabi, Ritesh Thawkar, Sara Ghaboura +6Longitudinal Imaging Magnetic Resonance Imaging
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · Department of Health Abu Dhabi, Abu Dhabi, UAE · Fatima College of Health Sciences, Abu Dhabi, UAE +1
CT- $Δ$ Bench: A Benchmark for Longitudinal 3D Medical Imaging Difference Reporting with Vision-Language Models
Aug 12, 2026Kegeng Tang, Jingbo Wang, Shaogang Ren +1Longitudinal Imaging Report
University of Tennessee at Chattanooga
Parcel2Progression: An Anatomy-aware Longitudinal Framework for Alzheimer's Disease Diagnosis
Aug 9, 2026Madhumitha Venkatesh, Shanawaj S Madarkar, Konda Reddy MopuriLongitudinal Imaging Alzheimer
Indian Institute of Technology Hyderabad, India
SpecF2M: A Spectral-Aware Multi-task Network Estimating Axial Length and Refractive Error from Pediatric Fundus Photographs
Aug 7, 2026Mengxian He, Xinyue Liu, Yunyun Sun +5Fundus Images Longitudinal Imaging
The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong · Guangdong Laboratory of Machine Perception and Intelligent Computing, Shenzhen MSU-BIT University, China · Capital Medical University, China +1
Longitudinal 3D Foundation Modeling for Neoadjuvant Breast Cancer Response Prediction from Serial DCE-MRI
Aug 7, 2026Fidel Omar Tito Cruz, Neda Ghafouri, Zengyan Wang +3Longitudinal Imaging Tumor
University of Central Florida, Orlando, FL, USA
Beyond Random Partitioning: Unsupervised Spatio-Temporal Stratification for Cohort Balancing in Longitudinal Medical Imaging
Jul 29, 2026Qinghui Liu, Jon André Ottesen, Atle Bjørnerud +1Longitudinal Imaging Partitioning
Department of Physics and Computational Radiology, Oslo University Hospital, Norway
TRACE-PCa: Predicting Prostate Cancer Progression from Longitudinal MRI During Active Surveillance
Jul 15, 2026Hongye Zeng, Shreeram Athreya, Dingyuan Dai +4Prostate Magnetic Resonance Imaging Prognostics
Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, USA · Department of Electrical & Computer Engineering, University of California, Los Angeles, Los Angeles, USA · Department of Urology, University of California, Los Angeles, Los Angeles, USA +2
Graph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction
Jul 6, 2026Johannes Kiechle, Richard Osuala, Daniel M. Lang +5Prognostics Longitudinal Imaging
Technical University of Munich · TUM University Hospital Rechts der Isar · Helmholtz Munich +4
Physics-Grounded Disentangled Flow Modeling for Brain Disease Progression Trajectory
Jun 26, 2026Jun Wang, Peirong LiuLongitudinal Imaging Disease
Department of Electrical and Computer Engineering, Data Science and AI Institute, Johns Hopkins University
E-MRL: Cross-view Aligned Evidence-driven Multimodal Reinforcement Learning for Reliable 3D Tumor Analysis
Jun 22, 2026Sijing Li, Zhongwei Qiu, Zhuoya Wang +6Medical Vision-Language Models Multimodal Clinical Data
Zhejiang University · DAMO Academy, Alibaba Group · Hupan Lab +3
OphthaDT: Generative Digital Twins for Forecasting Visual Acuity Trajectories in Ophthalmology
Jun 20, 2026Pietro Belligoli, Nikita Makarov, Sayedali Shetab Boushehri +3Ophthalmology Longitudinal Imaging
Computational Sciences Center of Excellence, Roche, Penzberg, Germany · Technical University of Munich, Munich, Germany · Computational Health Center, Helmholtz Munich, Munich, Germany +3
Learning Directional Semantic Transitions for Longitudinal Chest X-ray Analysis
Jun 14, 2026Zhangfeng Hu, Zefan Yang, Ge Wang +4Chest X-Ray Longitudinal Imaging
Rensselaer Polytechnic Institute, Troy, NY, USA · Stanford University, Stanford, CA, USA · Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
StrokeTimer: Robust Representation Learning for Ischemic Stroke Onset-Time Estimation from Non-contrast CT
Jun 3, 2026Weiru Wang, Susanne G. H. Olthuis, Elizaveta Lavrova +4Stroke Longitudinal Imaging
Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands · Graduate School of Life Sciences, Utrecht University, Utrecht, The Netherlands · Department of Neurology, Maastricht University Medical Centre+, Maastricht, The Netherlands +3
Response-Aware Multimodal Learning for Post-Treatment Visual Acuity Forecasting
May 30, 2026Phuoc-Nguyen Bui, Van-Vi Vo, Duc-Tai Le +5Longitudinal Imaging Multimodal Learning
Research Convergence Institute, Sungkyunkwan University, Korea · Dept. of AI Systems Engineering, Sungkyunkwan University, Korea · College of Computing and Informatics, Sungkyunkwan University, Korea +2
Treatment-Conditioned Diffusion for Forecasting Neurodegenerative Disease Progression
May 28, 2026Danylo Boiko, Viktoriia MishkurovaMultimodal Neuroimaging Text-Conditioned Diffusion Model
Innoloft Inc. 701 Brazos Street, Austin, TX 78701, USA · Bogomolets National Medical University Beresteiskyi Avenue, 34, Kyiv, 03057, Ukraine
Prediction of Rectal Cancer Regrowth from Longitudinal Endoscopy
May 13, 2026Jorge Tapias Gomez, Despoina Kanata, Aneesh Rangnekar +8Colonoscopy Longitudinal Imaging
School of Computer Science, Cornell University and Cornell Tech, New York, NY · Department of Surgery, Colorectal Service, Memorial Sloan Kettering Cancer Center, New York, NY · Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY +3
FLUX: Geometry-Aware Longitudinal Flow Matching with Mixture of Experts
May 9, 2026Josue Ortega Caro, Yongxu Zhang, Hannah M Batchelor +3Conditional Flow Matching Longitudinal
Yale University
iTRIALSPACE: Programmable Virtual Lesion Trials for Controlled Evaluation of Lung CT Models
May 7, 2026Fakrul Islam Tushar, Umme Hafsa Momy, Joseph Y. Lo +1Lung-Nodule Artificial Intelligence Cone-Beam Computed Tomography
Department of Radiology and Imaging Sciences, University of Arizona · Department of Biomedical Engineering, Florida International University · Center for Virtual Imaging Trials, Department of Radiology, Duke University Medical Center
Disentangled Anatomy-Disease Diffusion (DADD) for Controllable Ulcerative Colitis Progression Synthesis
May 3, 2026Umut Dundar, Alptekin TemizelLongitudinal Imaging U-Net
Graduate School of Informatics Middle East Technical University, Turkey
Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model
Apr 24, 2026Nivetha Jayakumar, Swakshar Deb, Bahram Jafrasteh +2Longitudinal Imaging Multimodal Neuroimaging
Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, 22903, VA, USA · Department of Radiology, Weill Cornell Medical College, Cornell University, Ithaca, 14850, NY, USA · Department of Electrical and Computer Engineering, Department of Computer Science, University of Virginia, Charlottesville, 22903, VA, USA
Training-inference input alignment outweighs framework choice in longitudinal retinal image prediction
Apr 18, 2026Liyin Chen, Nazlee Zebardast, Mengyu Wang +2Longitudinal Imaging U-Net
Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA · Broad Institute of MIT and Harvard, Cambridge, MA, USA · Harvard Ophthalmology AI Lab, Schepens Eye Research Institute, Harvard Medical School, Boston, MA, USA +1
Longitudinal Lesion Inpainting in Brain MRI via 3D Region Aware Diffusion
Mar 5, 2026Zahra Karimaghaloo, Dumitru Fetco, Haz-Edine Assemlal +2Longitudinal Imaging Functional Magnetic Resonance Imaging
Clario part of Thermofisher Scientific, Montreal, Canada · Concordia University, Montreal, Canada

Sep 28, 2026, cs.CVJ/K move · Enter open · S save

RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports

Pedro R. A. S. Bassi, Wenxuan Li, Hanxue Gu, Jieneng Chen+10

Johns Hopkins University · Harvard Medical School · Massachusetts General Hospital+4

Tumor Segmentation Longitudinal Imaging Cnn-Transformer Tradeoff

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Multi-tumor segmentation is important for early cancer detection and allows radiologists to visualize, verify, and understand AI predictions. However, tumor segmentation masks are expensive, time-consuming, and unavailable for many tumor types in public data. Instead, hospitals have vast, readily available data that can guide segmentation: radiology reports, longitudinal images, and multi-phase images. We use this readily available data to substitute for tumor masks in training AI for tumor segmentation. To this end, we propose a new architecture, RT-Super. It has a teacher network, which analyzes the patient's longitudinal images and reports to create high-quality tumor masks. These masks train a student network, which sees a single image and no report. At inference, when longitudinal images and reports are unavailable, we use the student. RT-Super uses a new CNN-Transformer architecture and novel Consistency Losses that exploit tumor location consistency across longitudinal images. We train RT-Super to segment esophagus, uterus and spleen tumors, which have few or no public masks. Even without training masks, RT-Super can segment these tumors and surpass public AI models. Overall, we demonstrate that learning from longitudinal images, multi-phase images, and reports can overcome mask scarcity and advance multi-cancer detection and segmentation. Code: https://github.com/MrGiovanni/RT-Super

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