Alzheimer's Disease Progression Prediction

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1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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MMAP: Multimodal Missing-Aware Pretraining for Longitudinal Alzheimer's Prediction
Sep 22, 2026Fiona Kekwick, Matthew Baugh, Bernhard Kainz +2Missing-Modality Learning Cross-Modal Alignment
Department of Computing, Imperial College London, London, UK · FAU Erlangen-Nürnberg, Erlangen, Germany · Department of Brain Sciences, Imperial College London, London, UK +1
Parcel2Progression: An Anatomy-aware Longitudinal Framework for Alzheimer's Disease Diagnosis
Aug 9, 2026Madhumitha Venkatesh, Shanawaj S Madarkar, Konda Reddy MopuriLongitudinal Medical Image Analysis Alzheimer's Disease Classification
Indian Institute of Technology Hyderabad, India
Calibrated Alzheimer's Conversion Risk in Mild Cognitive Impairment: Persistent Homology of Clinical Trajectories with Conformal Guarantees
Jul 20, 2026Navin BondadeClinical Outcome Prediction Alzheimer's Disease Classification
Institute of Health Informatics, University College London
Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts
Jul 13, 2026Christelle Schneuwly Diaz, Narmina Baghirova, Duy-Thanh Vu +4Missing-Modality Learning Learning with Missing Data
Platform of Bioinformatics, Lausanne University Hospital. · Faculty of Biology and Medicine, University of Lausanne. · 3Leenaards Memory Centre, Lausanne University Hospital. +1
Predicting Early Stages Of Alzheimer's Disease And Identifying Key Biomarkers Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies
Jul 2, 2026Debopriya GhoshClinical Prediction Alzheimer's Disease Classification
ENC-ODE: Event-level Neurodegenerative Modeling in Continuous Time with Neural ODEs
Jun 29, 2026Yujee Song, Seunghun Baek, Guorong Wu +1Latent Dynamics Modeling Longitudinal EHR Modeling
Pohang University of Science and Technology, Pohang, South Korea · University of North Carolina at Chapel Hill, Chapel Hill, USA
Neural operator-based digital twins for modeling amyloid- $β$ and tau propagation and treatment optimization in Alzheimer's disease
Jun 23, 2026Xiaofeng Xu, Tingting Dan, Zifan Zhou +3Alzheimer's Disease PDE Operator Learning
Department of Mathematics, Pennsylvania State University, University Park, PA 16802, USA · Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA · School of Electrical Engineering and Computer Science, Pennsylvania State University, University Park, PA 16802, USA +3
Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning
Jun 23, 2026Arya Hariharan, Shreyank N Gowda, Anala M RClinical Outcome Prediction Alzheimer's Disease Classification
Department of Computer Science and Engineering, R.V. College of Engineering, Bengaluru, Karnataka, India · School of Computer Science, University of Nottingham, Nottingham, United Kingdom
SMART: A Flexible, Interpretable, and Scalable Spatio-temporal Brain Atlas from High-Resolution Imaging Data
Jun 17, 2026John Kalkhof, Boris Gutman, Emile d'Angremont +2Longitudinal Medical Image Analysis Neuroimaging
IIT · Inria Center at University Côte d’Azur Sophia Antipolis, France · Illinois Institute of Technology Chicago, IL, USA +4
Bayesian Networks with Latent Time Embedding for Stage-Aware Causal Modeling of Alzheimer's Disease Progression
Jun 14, 2026Nguyen Linh Dan LeCausal Effect Estimation Structural Causal Models
Department of Biomedical Engineering The University of Melbourne, Melbourne, Australia
iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis
Jun 12, 2026Farica Zhuang, Seong Woo Han, Zixuan Wen +3Mixture of Experts Survival Analysis
University of Pennsylvania · Yale University
Transition-Based Digital Twin Modelling for Alzheimer's Disease under Sparse Longitudinal Data
Jun 8, 2026Yinyu Huang, Yilin Zhang, Sofia Michopoulou +2Irregular Time-Series Modeling Clinical Outcome Prediction
School of Electronics and Computer Science, University of Southampton, UK · University Hospital Southampton NHS Foundation Trust, Southampton, UK · Faculty of Medicine, University of Southampton, UK
Reconstructing and forecasting disease trajectories of patients with Alzheimer's disease using routine data in resource-constrained settings
Jun 5, 2026Ratnadeep Das, Atri Chatterjee, Sitikantha RoyAlzheimer's Disease Clinical Outcome Prediction
Yardi School of Artificial Intelligence (ScAI), Indian Institute of Technology Delhi, Hauz Khas, Delhi, 110016, India · Department of Neurology, Vardhman Mahavir Medical College and Safdarjung Hospital, Delhi, 110029, India · Department of Applied Mechanics, Indian Institute of Technology Delhi, Hauz Khas, Delhi, 110016, India
Bayesian meta-learning for modeling Alzheimer's disease progression
Jun 1, 2026Clara Hoffmann, Nadja KleinAlzheimer's Disease Meta-Learning
Scientific Computing Center, Karlsruhe Institute of Technology, Germany
Adaptive Temporal Gating of Longitudinal Magnetic Resonance Imaging for Alzheimer's Prediction
May 27, 2026Alireza Moayedikia, Sara Fin, Alicia Troncoso Lora +1Hybrid CNN-Transformer Architectures Longitudinal Medical Image Analysis
School of Business Law and Entrepreneurship, Swinburne University of Technology, Melbourne, VIC, Australia · Australian Regenerative Medicine Institute, Monash University, Melbourne, VIC, Australia · Data Science & Big Data Lab, Universidad Pablo de Olavide, Seville, Spain +1
Forecasting Medium-Horizon Alzheimer's Disease Progression: Residual Gap-Aware Transformers for 24-Month CDR-SB Change from ADNI Clinical and Biomarker Histories
May 4, 2026Ran Tong, Tong Wang, Lanruo Wang +1Irregular Time-Series Modeling Alzheimer's Disease
Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX 75080, United States · Department of Plant Science and Landscape Architecture, University of Connecticut, Storrs, CT, United States · Naveen Jindal School of Management, University of Texas at Dallas, Richardson, TX 75080, United States +1
GeoSAE: Geometric Prior-Guided Layer-Wise Sparse Autoencoder Annotation of Brain MRI Foundation Models
May 3, 2026Favour Nerrise, Lucy Yin, Mohammad H. Abbasi +2Alzheimer's Disease Medical Imaging Foundation Models
Stanford University · Stanford University, CA
PROMISE-AD: Progression-aware Multi-horizon Survival Estimation for Alzheimer's Disease Progression and Dynamic Tracking
Apr 30, 2026Qing Lyu, Jeremy Hudson, Mohammad Kawas +3Alzheimer's Disease Survival Analysis
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510 USA · Department of Radiology Oncology, Wake Forest University School of Medicine, Winston-Salem, NC 27157 USA · Department of Applied Mathematics & Statistics and Department of Computer Science, Stony Brook University, Stony Brook, NY 11794 USA
Toward Personalized Digital Twins for Cognitive Decline Assessment: A Multimodal, Uncertainty-Aware Framework
Apr 29, 2026Bulent Soykan, Gulsah Hancerliogullari Koksalmis, Hsin-Hsiung Huang +1Alzheimer's Disease Clinical Outcome Prediction
Department of Mechanical, Industrial & Manufac. Eng. The University of Toledo Toledo, OH, USA · Department of Industrial Eng. and Mngt. Systems University of Central Florida Orlando, FL, USA · Department of Statistics and Data Science University of Central Florida Orlando, FL, USA +1
Evaluating TabPFN for Mild Cognitive Impairment to Alzheimer's Disease Conversion in Data Limited Settings
Apr 29, 2026Brad Ye, Bulent Soykan, Gulsah Hancerliogullari Koksalmis +2Tabular Foundation Models Alzheimer's Disease
Department of Medicine, University of Central Florida College of Medicine, Orlando, FL, USA · NIMBUS Lab, University of Central Florida · Department of Mechanical, Industrial & Manufacturing Eng., The University of Toledo, Toledo, OH, USA +3
TEMPO: Transformers for Temporal Disease Progression from Cross-Sectional Data
Apr 25, 2026Hongtao Hao, Joseph L. AusterweilAlzheimer's Disease Healthcare
University of Wisconsin—Madison, USA · Chiba Institute of Technology, Japan & University of Wisconsin—Madison, USA
CognitiveTwin: Robust Multi-Modal Digital Twins for Predicting Cognitive Decline in Alzheimer's Disease
Apr 24, 2026Bulent Soykan, Gulsah Hancerliogullari Koksalmis, Hsin-Hsiung Huang +1Alzheimer's Disease Multimodal Robustness
Department of Mechanical, Industrial & Manufacturing Eng. The University of Toledo Toledo, OH, USA · Department of Industrial Eng. and Mngt. Systems University of Central Florida Orlando, FL, USA · Department of Statistics and Data Science University of Central Florida Orlando, FL, USA +1
SDE-Driven Spatio-Temporal Hypergraph Neural Networks for Irregular Longitudinal fMRI Connectome Modeling in Alzheimer's Disease
Mar 20, 2026Ruiying Chen, Yutong Wang, Houliang Zhou +3Irregular Time-Series Modeling Alzheimer's Disease
Lehigh University, Bethlehem, PA, USA · New York University, New York, NY, USA · University of Pennsylvania, Philadelphia, PA, USA
Anatomically Guided Latent Diffusion for Brain MRI Progression Modeling
Jan 21, 2026Cheng Wan, Bahram Jafrasteh, Ehsan Adeli +2Latent Diffusion Models Longitudinal Medical Image Analysis
Cornell University · Weill Cornell Medicine · Stanford University +1
AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders
Nov 8, 2025Ayantika Das, Arunima Sarkar, Keerthi Ram +1Medical Image Synthesis Alzheimer's Disease Progression Prediction
Department of Electrical Engineering, Indian Institute of Technology Madras (IITM), Chennai 600036, India · Sudha Gopalakrishnan Brain Centre (SGBC), IITM, Chennai 600036, India

Sep 22, 2026, cs.CVJ/K move · Enter open · S save

MMAP: Multimodal Missing-Aware Pretraining for Longitudinal Alzheimer's Prediction

Fiona Kekwick, Matthew Baugh, Bernhard Kainz, Paul M. Matthews+1

Department of Computing, Imperial College London, London, UK · FAU Erlangen-Nürnberg, Erlangen, Germany · Department of Brain Sciences, Imperial College London, London, UK+1

Missing-Modality Learning Cross-Modal Alignment Multimodal Pretraining Multimodal Contrastive Learning Alzheimer's Disease Progression Prediction

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Clinical decision making heavily relies on predicting the disease progression trajectory by seeking to understand patient's health status which is characterised by multimodal medical data. AI holds great potential for learning useful representations from multimodal medical data to predict disease progression and aid clinical decision making. However, development of predictive AI models is constrained by missing modalities and incomplete tabular data frequently occurring in medical datasets. In addition, disease labels alone may only provide limited supervisory signals for learning representations from high-dimensional multimodal data. Here, we present MMAP, a novel Multimodal Missing-aware Alignment Pretraining method for learning image-tabular representations from incomplete data. An image encoder is pretrained with efficient sigmoid contrastive learning combined with generative reconstruction. A tabular encoder is built upon a tabular foundation model. A missing token generator enables the two encoders to take incomplete data as input, enabling the model to be robust against missing modalities, either with missing images or missing tabular data. We evaluate the clinical usefulness of the learnt multimodal representations on two challenging longitudinal clinical tasks for Alzheimer's disease: predicting disease stage conversion and predicting amyloid status. The proposed method outperforms strong multimodal and unimodal baselines.

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