Alzheimer's Disease Classification

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Alzheimer’s disease classification aims to distinguish cognitively healthy people, mild cognitive impairment, and Alzheimer’s disease, and to identify who may progress from one stage to another. Common inputs include MRI and PET scans, EEG, speech or handwriting, and clinical or cognitive measures, analyzed with methods such as convolutional networks, graph learning, transformers, and multimodal fusion. Researchers also study interpretable predictions, calibrated uncertainty, longitudinal models, and systems that can work when some tests are missing or costly. A central challenge is whether strong results transfer across cohorts, populations, and clinical settings, since data leakage, inconsistent evaluation, and changes in scanners or speech collection can undermine apparent accuracy.

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93 papers10 papers in the last four weeks, down 9% on the four weeks before. 0.1% of all new papers.

PRA-PoE: Robust Multimodal Alzheimer's Diagnosis with Arbitrary Missing Modalities
May 13, 2026Guangqian Yang, Ye Du, Wenlong Hou +2Missing-Modality Learning Alzheimer's Disease
Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China · Department of Technology Management for Innovation, The University of Tokyo,2026 Japan · Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong SAR, China
BrainAnytime: Anatomy-Aware Cross-Modal Pretraining for Brain Image Analysis with Arbitrary Modality Availability
May 13, 2026Guangqian Yang, Tong Ding, Wenlong Hou +4Missing-Modality Learning Masked Autoencoders
Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China · Department of Technology Management for Innovation, The University of Tokyo, Japan · Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong SAR, China
PromptDx: Differentiable Prompt Tuning for Multimodal In-Context Alzheimer's Diagnosis
May 9, 2026Lujia Zhong, Yihao Xia, Shuo Huang +2Multimodal ICL Medical Diagnosis
Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California · Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California · Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California
NeuroAgent: LLM Agents for Multimodal Neuroimaging Analysis and Research
May 7, 2026Lujia Zhong, Yihao Xia, Jianwei Zhang +4Multi-Agent LLM Systems LLM Agent Orchestration
1Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California · 2Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California · 3Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California
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
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
Task-guided Spatiotemporal Network with Diffusion Augmentation for EEG-based Dementia Diagnosis and MMSE Prediction
Apr 27, 2026Xiaoyu Zheng, Xu Tian, Bin Jiao +4Alzheimer's Disease Electroencephalography
Xinjiang Engineering Research Center of Big Data and Intelligent Software, School of Software, Xinjiang University, Urumqi 830091, China. · Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China. · Department of Neurology, Xiangya Hospital, Central South University, Changsha 410083, China. +1
NeuroAPS-Net: Neuro-Anatomically Aware Point Cloud Representation for Efficient Alzheimer's Disease Classification
Apr 24, 2026Towhidul Islam, Mufti MahmudEfficient Neural Network Inference Point Cloud Classification
ICS Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia · SDAIA-KFUPM JRC for AI, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia · IRC for Bio Systems and Machines, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
REVEAL: Multimodal Vision-Language Alignment of Retinal Morphometry and Clinical Risks for Incident AD and Dementia Prediction
Apr 20, 2026Seowung Leem, Lin Gu, Chenyu You +2Alzheimer's Disease Medical Imaging
J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, United2026 States · Research Institute of Electrical Communication, Tohoku University, Japan · Department of Applied Mathematics & Statistics, Stony Brook University, United StatesApr +1
Improved Multiscale Structural Mapping with Supervertex Vision Transformer for the Detection of Alzheimer's Disease Neurodegeneration
Apr 16, 2026Geonwoo Baek, David H. Salat, Ikbeom JangAlzheimer's Disease Vision Transformer
Department of Computer Science & Engineering, Hankuk University of Foreign Studies, Seoul, Republic of Korea · Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA · Department of Radiology, Harvard Medical School, Boston, MA, USA +1
Efficient Handwriting-Based Alzheimer,s Disease Diagnosis Using a Low-Rank Mixture of Experts Deep Learning Framework
Apr 14, 2026Wu Wang, Yuang Cheng, Fouzi Harrou +1Alzheimer's Disease Classification Mixture-of-Experts Models
Center for Applied Statistics and School of Statistics Renmin University of China Beijing 100872, China · King Abdullah University of Science and Technology (KAUST) Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division Thuwal 23955-6900, Saudi Arabia
Early Detection of Alzheimer's Disease Using Explainable Machine Learning on Clinical Biomarkers: A Multi-Class Classification Study Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset
Apr 14, 2026Afshan HashmiAlzheimer's Disease Classification
TRDC, Tuwaiq Academy, Riyadh, Saudi Arabia
AD-CARE: A Guideline-grounded, Modality-agnostic LLM Agent for Real-world Alzheimer's Disease Diagnosis with Multi-cohort Assessment, Fairness Analysis, and Reader Study
Mar 26, 2026Wenlong Hou, Sheng Bi, Guangqian Yang +16Alzheimer's Disease Classification Clinical Decision Support
Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China. · Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China. · Amazon, USA. +10
AD-Reasoning: Multimodal Guideline-Guided Reasoning for Alzheimer's Disease Diagnosis
Mar 25, 2026Qiuhui Chen, Yushan Deng, Xuancheng Yao +1Alzheimer's Disease Classification Medical Diagnosis
School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China · School of Computer Science, Shanghai Jiao Tong University, Shanghai, China
Impact of automatic speech recognition quality on Alzheimer's disease detection from spontaneous speech: a reproducible benchmark study with lexical modeling and statistical validation
Mar 18, 2026Himadri S SamantaSpeech-Based Dementia Detection Alzheimer's Disease Classification
Independent Researcher, Austin, Texas, USA
Agentic Cognitive Profiling: Realigning Automated Alzheimer's Disease Detection with Clinical Construct Validity
Mar 18, 2026Jiawen Kang, Kun Li, Dongrui Han +5Alzheimer's Disease Classification
The Chinese University of Hong Kong
3D MRI-Based Alzheimer's Disease Classification Using Multi-Modal 3D CNN with Leakage-Aware Subject-Level Evaluation
Mar 18, 2026Md Sifat, Sania Akter, Akif Islam +5Alzheimer's Disease Classification Multimodal Classification
Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh · School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu, Japan · Department of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, Bangladesh
Tabular LLMs for Interpretable Few-Shot Alzheimer's Disease Prediction with Multimodal Biomedical Data
Mar 17, 2026Sophie Kearney, Shu Yang, Zixuan Wen +8Alzheimer's Disease Classification Tabular Foundation Models
University of Pennsylvania, Philadelphia, PA, USA · Stony Brook University, Stony Brook, NY, USA · University of North Carolina at Chapel Hill, Chapel Hill, NC, USA +2
Federated Learning for Privacy-Preserving Medical AI
Mar 16, 2026Tin HoangDifferentially Private Federated Learning Differential Privacy
School of Computer Science and Electrical and Electronic Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, UK
LoV3D: Grounding Cognitive Prognosis Reasoning in Longitudinal 3D Brain MRI via Regional Volume Assessments
Mar 12, 2026Zhaoyang Jiang, Zhizhong Fu, David McAllister +2Longitudinal Medical Image Analysis Alzheimer's Disease Classification
School of Health & Wellbeing, University of Glasgow, Glasgow, UK · School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China · Institute of Health Informatics, University College London, London, UK
Multimodal Deep Learning for Dynamic and Static Neuroimaging: Integrating MRI and fMRI for Alzheimer Disease Analysis
Mar 9, 2026Anima Kujur, Zahra MonfaredAlzheimer's Disease Classification Neuroimaging
Interdisciplinary Centre for Scientific Computing (IWR), Department of Mathematics and Computer Science, Heidelberg University, Germany
Enhancing SHAP Explainability for Diagnostic and Prognostic ML Models in Alzheimer Disease
Mar 6, 2026Pablo Guillén, Enrique Frias-MartinezExplainability Evaluation Explanation Stability
MIU City University, Miami, FL, USA · Research Institute UNIR iTED, Universidad Internacional de La Rioja (UNIR), Spain
MIRAGE: Knowledge Graph-Guided Cross-Cohort MRI Synthesis for Alzheimer's Disease Prediction
Mar 2, 2026Guanchen Wu, Zhe Huang, Yuzhang Xie +6Alzheimer's Disease Classification Learning with Missing Data
Emory University, Atlanta, USA · Cornell University, New York, USA
Interpretable Cross-Network Attention for Resting-State fMRI Representation Learning
Feb 28, 2026Karanpartap Singh, Adam Turnbull, Mohammad Abbasi +3Brain Network Analysis Functional Connectivity
Stanford University, Stanford, CA 94305
Multiple Inputs and Mixwd data for Alzheimer's Disease Classification Based on 3D Vision Transformer
Feb 28, 2026Juan A. Castro-Silva, Maria N. Moreno Garcia, Diego H. Peluffo-OrdoñezAlzheimer's Disease Classification Multimodal Classification
Faculty of Engineering, Universidad Surcolombiana, Neiva 410002, Colombia · College of Computing, Mohammed VI Polytechnic University, Lot 660, Hay Moulay Rachid, Ben Guerir 43150, Morocco
MINT: Multimodal Imaging-to-Speech Knowledge Transfer for Early Alzheimer's Screening
Feb 27, 2026Vrushank Ahire, Yogesh Kumar, Anouck Girard +1Cross-Modal Knowledge Distillation Alzheimer's Disease
Department of Computer Science and Engineering, Indian Institute of Technology Ropar, India · Embry-Riddle Aeronautical University, Florida, USA
An Explainable Ensemble Framework for Alzheimer's Disease Prediction Using Structured Clinical and Cognitive Data
Feb 26, 2026Nishan MitraAlzheimer's Disease Classification Clinical Prediction
Computer Science & Engineering (AI&ML), Institute of Engineering & Management, Kolkata, India
EMAD: Evidence-Centric Grounded Multimodal Diagnosis for Alzheimer's Disease
Feb 22, 2026Qiuhui Chen, Xuancheng Yao, Zhenglei Zhou +2Medical VLMs Explainable Medical Image Analysis
East China University of Science and Technology · Shanghai Jiao Tong University · Tencent
LERD: Latent Event-Relational Dynamics for Neurodegenerative Classification
Feb 20, 2026Yicheng Feng, Hairong Chen, Ziyu Jia +2Alzheimer's Disease Classification Latent Dynamics Modeling
Section of Health Data Science & AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark · Institute of Information Science, Beijing Jiaotong University, Beijing, China · College of Computing and Data Science, Nanyang Technological University, Singapore +3
Probability-Invariant Random Walk Learning on Gyral Folding-Based Cortical Similarity Networks for Alzheimer's and Lewy Body Dementia Diagnosis
Feb 19, 2026Minheng Chen, Tong Chen, Chao Cao +4Alzheimer's Disease Classification Brain Network Analysis
Department of Computer Science and Engineering, University of Texas at Arlington, United States · School of Computing, University of Georgia, United States · Department of Psychiatry, University of Cambridge, United Kingdom +1

May 13, 2026J/K move · Enter open · S save

PRA-PoE: Robust Multimodal Alzheimer's Diagnosis with Arbitrary Missing Modalities

Guangqian Yang, Ye Du, Wenlong Hou, Qian Niu+1

Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China · Department of Technology Management for Innovation, The University of Tokyo,2026 Japan · Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong SAR, China

Missing-Modality Learning Alzheimer's Disease Multimodal Robustness Medical Diagnosis Cross-Modal Alignment Learning with Missing Data+2

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Missing modalities are prevalent in real-world Alzheimer's disease (AD) assessment and pose a significant challenge to multimodal learning, particularly when the distribution of observed modality subsets differs between training and deployment. Such missingness pattern mismatch induces a conditional representation shift across modality subsets. Existing approaches that rely on implicit imputation or modality synthesis often fail to explicitly model modality availability and uncertainty, leading to overconfident dependence on synthesized features, reduced robustness, and miscalibrated uncertainty estimates. To address these limitations, we propose PRA-PoE, an incomplete multimodal learning framework that is equipped with Prototype-anchored Representation Alignment (PRA) and an Uncertainty-aware Product of Experts (UA-PoE) fusion mechanism. First, PRA uses learnable global prototypes and availability-conditioned tokens to encode modality availability, distinguish observed from missing modalities, re-synthesize features for missing modalities, and adaptively refine observed representations to align latent spaces across modality subsets, with the goal of reducing representation shift under varying missingness patterns. Second, UA-PoE models each modality as a Gaussian expert and performs closed-form Product of Experts fusion, where experts with higher uncertainty are automatically down-weighted via lower precision, improving uncertainty reliability. We evaluate PRA-PoE under a clinically realistic protocol by training with naturally missing data and testing on all non-empty modality combinations. PRA-PoE consistently outperforms the state-of-the-art across datasets, achieving a 5.4% relative improvement in average accuracy on ADNI and a 10.9% relative gain in average F1 on OASIS-3 over the strongest baseline across all non-empty modality subsets.