Reliable Alzheimer's disease (AD) diagnosis increasingly relies on multimodal assessments combining structural Magnetic Resonance Imaging (MRI) and Electronic Health Records (EHR). However, deploying these models is bottlenecked by modality missingness, as MRI scans are expensive and frequently unavailable in many patient cohorts. Furthermore, synthesizing de novo 3D anatomical scans from sparse, high-dimensional tabular records is technically challenging and poses severe clinical risks. To address this, we introduce MIRAGE, a novel framework that reframes the missing-MRI problem as an anatomy-guided cross-modal latent distillation task. First, MIRAGE leverages a Biomedical Knowledge Graph (KG) and Graph Attention Networks to map heterogeneous EHR variables into a unified embedding space that can be propagated from cohorts with real MRIs to cohorts without them. To bridge the semantic gap and enforce physical spatial awareness, we employ a frozen pre-trained 3D U-Net decoder strictly as an auxiliary regularization engine. Supported by a novel cohort-aggregated skip feature compensation strategy, this decoder acts as a rigorous structural penalty, forcing 1D latent representations to encode biologically plausible, macro-level pathological semantics. By exclusively utilizing this distilled "diagnostic-surrogate" representation during inference, MIRAGE completely bypasses computationally expensive 3D voxel reconstruction. Experiments demonstrate that our framework successfully bridges the missing-modality gap, improving the AD classification rate by 13% compared to unimodal baselines in cohorts without real MRIs.
Erasmus MC（伊拉斯谟医学中心）；Maastricht University Medical Center（马斯特里赫特大学医学中心）；University Medical Center Groningen（格罗宁根大学医学中心）；University Medical Center Utrecht（乌得勒支大学医学中心）；Radboud University Medical Center（拉德堡德大学医学中心）；Leiden University Medical Center（莱顿大学医学中心）；Leiden University（莱顿大学）
This work validates the generalizability of MRI-based classification of Alzheimer's disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI). We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive pre-processing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross-validation in the ADNI (334 AD, 520 CN). Tr…
Manchester Metropolitan University（曼彻斯特城市大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
Computer-aided early diagnosis of Alzheimer's disease (AD) and its prodromal form mild cognitive impairment (MCI) based on structure Magnetic Resonance Imaging (sMRI) has provided a cost-effective and objective way for early prevention and treatment of disease progression, leading to improved patient care. In this work, we have proposed a novel computer-aided approach for early diagnosis of AD by introducing an explainable 3D Residual Attention Deep Neural Network (3D ResAttNet) for end-to-end learning from sMRI scans. Different from the existing approaches, the novelty of our approach is thre…
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…
Increasing effort in brain image analysis has been dedicated to early diagnosis of Alzheimer's disease (AD) based on neuroimaging data. Most existing studies have been focusing on binary classification problems, e.g., distinguishing AD patients from normal control (NC) elderly or mild cognitive impairment (MCI) individuals from NC elderly. However, identifying individuals with AD and MCI, especially MCI individuals who will convert to AD (progressive MCI, pMCI), in a single setting, is needed to achieve the goal of early diagnosis of AD. In this paper, we propose a deep ordinal ranking model f…
PANDA - Prototype-Anchored Alignment for Partially Unpaired Multimodal Learning, with Applications to Alzheimers MRI and TCGA Pathology
Friedrich-Alexander-Universität（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学）；Klinikum Nürnberg, Paracelsus Medical University（帕拉塞尔苏斯医科大学纽伦堡医院）；Ludwig-Maximilians-Universität München（慕尼黑大学）；National Institute of Mental Health and Neurosciences (NIMHANS)（国家精神卫生与神经科学研究院）
Early Alzheimer's Disease Detection from Retinal OCT Images: A UK Biobank Study
Department of Computer Engineering Isik University Istanbul, Turkey（计算机工程系伊斯肯大学）；Department of Artificial Intelligence and Data Engineering（人工智能与数据工程系）；Data Engineering Istanbul Technical University Istanbul, Turkey（数据工程系伊斯坦布尔技术大学）
A Generalizable Feature Extractor for Alzheimer's-Related Brain MRI Tasks
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University（麦吉尔大学蒙特利尔神经学研究所麦康奈尔脑成像中心）；Department of Biomedical Engineering, McGill University（麦吉尔大学生物医学工程系）
Longitudinal Boundary Sharpness Coefficient Slopes Predict Time to Alzheimer's Disease Conversion in Mild Cognitive Impairment: A Survival Analysis Using the ADNI Cohort
NeuroBridge: Bridging Multi-Task MRI Knowledge for Neurodegenerative Disease Diagnosis
Department of Mechanical Engineering, Boston University（波士顿大学机械工程系）；The Photonics Center, Boston University（波士顿大学光子中心）；Rafik B. Hariri Institute for Computing and Computational Science & Engineering, Boston University（波士顿大学拉菲克·B·哈里里计算与计算科学与工程研究所）；Department of Radiology, Boston University Chobanian & Avedisian School of Medicine（波士顿大学切博尼亚与阿维迪亚安医学学院放射科）；Department of Radiology, Boston Medical Center（波士顿医学中心放射科）；Department of Electrical and Computer Engineering, Boston University（波士顿大学电气与计算机工程系）；Department of Biomedical Engineering, Boston University（波士顿大学生物医学工程系）；Division of Materials Science and Engineering, Boston University（波士顿大学材料科学与工程系）
Modality Contribution Score - A Per-Patient Framework for Quantifying the Relative Diagnostic Contribution of Structural MRI and Amyloid PET in Alzheimer's Disease
Taipei Medical University（台北医学大学）；University of Memphis（孟菲斯大学）；Chitkara University（奇特卡拉大学）；University College London（伦敦大学学院）；UPES（石油与能源研究大学）；University of Eastern Finland（东芬兰大学）；CNRS（法国国家科学研究中心）；University Bordeaux（波尔多大学）；Bordeaux INP（波尔多国立理工学院）
A Multimodal Explainable Deep Learning Framework for Alzheimer's Disease Diagnosis using 3D Magnetic Resonance Imaging and Clinical Data
Osnabrück University（奥斯纳布吕克大学）；Rhodes University（罗德斯大学）；National Institute for Theoretical and Computational Sciences (NITheCS)（国家理论与计算科学研究所）；Madanapalle Institute of Technology and Science（马达纳帕莱理工学院）；Université Clermont Auvergne（克莱蒙奥弗涅大学）
Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET
Department of Artificial Intelligence, Hanyang University, Seoul, South Korea（人工智能系，翰阳大学，首尔，韩国）；Department of Electronic Engineering, Hanyang University, Seoul, South Korea（电子工程系，翰阳大学，首尔，韩国）；Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin, South Korea（计算机工程系，韩国外语大学， Yongin，韩国）；Division of AI Data Convergence, Hankuk University of Foreign Studies, Yongin, South Korea（AI数据融合系，韩国外语大学， Yongin，韩国）；Division of Language & AI, Hankuk University of Foreign Studies, Seoul, South Korea（语言与AI系，韩国外语大学，首尔，韩国）
Higher-Order Domain Generalization in Magnetic Resonance-Based Assessment of Alzheimer's Disease
Faculty of Engineering and Natural Sciences (VPA Lab), Sabanci University, Istanbul, Türkiye（工程与自然科学学院（VPA实验室），萨班奇大学，伊斯坦布尔，土耳其）；Department of Computer Science, Boston University（计算机科学系，波士顿大学）；Department of Medicine, Boston University Chobanian & Avedisian School of Medicine（医学系，波士顿大学乔班尼安与阿维迪西安医学院；计算机科学系和计算与数据科学学院，波士顿大学）；Department of Computer Science and Faculty of Computing and Data Sciences, Boston University
Riemannian Shape Analysis of the Corpus Callosum in Kendall Space: Aging and Alzheimer's Disease
African Institute for Mathematical Sciences (AIMS), Rwanda（非洲数学科学研究所（卢旺达））；African Institute for Mathematical Sciences (AIMS), Senegal（非洲数学科学研究所（塞内加尔））
GL-ICNN: An End-To-End Interpretable Convolutional Neural Network for the Diagnosis and Prediction of Alzheimer's Disease
Erasmus MC（伊拉斯姆斯医学中心）；University Medical Center Groningen（格罗宁根大学医学中心）；University Medical Center Utrecht（乌得勒支大学医学中心）；Radboud University Medical Center（拉德堡德大学医学中心）；Leiden University Medical Center（莱顿大学医学中心）；Amsterdam University Medical Center（阿姆斯特丹大学医学中心）；Maastricht University Medical Center（马斯特里赫特大学医学中心）
HoloDx: Knowledge- and Data-Driven Multimodal Diagnosis of Alzheimer's Disease
School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；Department of Neurology, Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属仁济医院神经内科）
University of Arizona（亚利桑那大学）；Mallinckrodt Institute of Radiology, Washington University（华盛顿大学马林克罗放射学研究所）；University of Utah（犹他大学）
The relationship between cognition and white matter hyperintensities (WMH) volumes often depends on the accuracy of the lesion segmentation algorithm used. As such, accurate detection and quantification of WMH is of great interest. Here, we use a deep learning-based WMH segmentation algorithm, StackGen-Net, to detect and quantify WMH on 3D FLAIR volumes from ADNI. We used a subset of subjects (n=20) and obtained manual WMH segmentations by an experienced neuro-radiologist to demonstrate the accuracy of our algorithm. On a larger cohort of subjects (n=290), we observed that larger WMH volumes c…
SynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and Resolutions
Dept. of Math and Computer Science（数学与计算机科学系）；Department of Neuroinflammation（神经炎症系）；School of Physics, Engineering and CS（物理、工程与计算机科学学校）；University of Hertfordshire（赫特福德大学）
Biomediq A/S（Biomediq公司）；University of Copenhagen（哥本哈根大学）；University College London（伦敦大学学院）
Disease progression modeling (DPM) using longitudinal data is a challenging task in machine learning for healthcare that can provide clinicians with better tools for diagnosis and monitoring of disease. Existing DPM algorithms neglect temporal dependencies among measurements and make parametric assumptions about biomarker trajectories. In addition, they do not model multiple biomarkers jointly and need to align subjects' trajectories. In this paper, recurrent neural networks (RNNs) are utilized to address these issues. However, in many cases, longitudinal cohorts contain incomplete data, which…
Alzheimer's disease (AD) is known as one of the major causes of dementia and is characterized by slow progression over several years, with no treatments or available medicines. In this regard, there have been efforts to identify the risk of developing AD in its earliest time. While many of the previous works considered cross-sectional analysis, more recent studies have focused on the diagnosis and prognosis of AD with longitudinal or time series data in a way of disease progression modeling (DPM). Under the same problem settings, in this work, we propose a novel computational framework that ca…
Interpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarking
Clemson University（克莱姆森大学）；University of New England（新英格兰大学）；UNSW Sydney（新南威尔士大学悉尼分校）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Tyree Foundation Institute of Health Engineering (IHealthE), UNSW Sydney（新南威尔士大学悉尼分校泰里基金会健康工程研究所）
Biomediq A/S（Biomediq公司）；Cerebriu A/S（Cerebriu公司）；University of Copenhagen（哥本哈根大学）；University College London（伦敦大学学院）；King’s College London（伦敦国王学院）
Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. Existing DPM algorithms neglect temporal dependencies among measurements, make parametric assumptions about biomarker trajectories, do not model multiple biomarkers jointly, and need an alignment of subjects' trajectories. In this paper, recurrent neural networks (RNNs) are utilized to address these issues. However, in many cases, longitudinal cohorts contain incomplete data, which hinders the application of standard RNNs and requires a pre-processing step such as imputation of the missing values…
Ulsan Ship and Ocean College, Ludong University（鲁东大学海洋学院（注：原文本为Ulsan Ship and Ocean College，结合鲁东大学烟台校区，推测此处“Ulsan”为笔误或翻译问题，准确应为鲁东大学相关学院））；School of Computing, Informatics, and Decision Systems Engineering, Arizona State University（亚利桑那州立大学计算、信息与决策系统工程学院）；Banner Alzheimer’s Institute and Banner Good Samaritan Pet Center（Banner阿尔茨海默病研究所（注：Banner Good Samaritan Pet Center为附属机构，按要求提取到机构级别））；School of Information and Electrical Engineering, Ludong University（鲁东大学信息与电气工程学院）；Shandong Province Key Lab of Digital Media Technology Shandong University of Finance and Economics（山东财经大学山东省数字媒体技术重点实验室（注：按要求提取到大学级别，实验室作为大学下属机构整体提取））；Department of Neurology Mayo Clinic Arizona（梅奥诊所亚利桑那州神经科（注：Mayo Clinic为机构名，Department of Neurology为科室，按要求提取到机构级别））
Cognitive decline due to Alzheimer's disease (AD) is closely associated with brain structure alterations captured by structural magnetic resonance imaging (sMRI). It supports the validity to develop sMRI-based univariate neurodegeneration biomarkers (UNB). However, existing UNB work either fails to model large group variances or does not capture AD dementia (ADD) induced changes. We propose a novel low-rank and sparse subspace decomposition method capable of stably quantifying the morphological changes induced by ADD. Specifically, we propose a numerically efficient rank minimization mechanism…
Conditional Evidence Reconstruction and Decomposition for Interpretable Multimodal Diagnosis
Department of Biomedical Engineering, New Jersey Institute of Technology（新泽西理工学院生物医学工程系）；Department of Computer Science and Engineering, The University of Texas at Arlington（德克萨斯大学阿灵顿分校计算机科学与工程系）；Department of Computer Science, Indiana University Indianapolis（印第安纳大学印第安纳波利斯分校计算机科学系）；School of Computing, University of Georgia（佐治亚大学计算科学学院）
An important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree of heterogeneity of shape and appearance. Traditional methods, based on image registration to a global template, historically fail to detect variable features of disease, as they utilise population-based analyses, suited primarily to studying group-average effects. In this paper we therefore take advantage of recent developments in generative deep learning to develop a method for simultaneous classification, or regre…
University College London（伦敦大学学院）；University of Côte d’Azur（蔚蓝海岸大学）；Inria Sophia Antipolis（法国国家信息与自动化研究所索菲亚·安提波利斯中心）
We introduce Disease Knowledge Transfer (DKT), a novel technique for transferring biomarker information between related neurodegenerative diseases. DKT infers robust multimodal biomarker trajectories in rare neurodegenerative diseases even when only limited, unimodal data is available, by transferring information from larger multimodal datasets from common neurodegenerative diseases. DKT is a joint-disease generative model of biomarker progressions, which exploits biomarker relationships that are shared across diseases. Our proposed method allows, for the first time, the estimation of plausibl…
Synthesizing Individualized Aging Brains in Health and Disease with Generative Models and Parallel Transport
Division of Biomedical Imaging, KTH Royal Institute of Technology, Stockholm, Sweden（生物医学成像系，皇家理工学院，斯德哥尔摩，瑞典）；Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institute, Stockholm, Sweden（临床老年医学系，阿尔茨海默病研究中心，卡罗林斯卡研究所，斯德哥尔摩，瑞典）；Division of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden（基因技术系，皇家理工学院，斯德哥尔摩，瑞典）；Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, MA, USA（计算机科学与人工智能实验室，麻省理工学院，马萨诸塞州，美国）；MedTechLabs, BioClinicum, Karolinska University Hospital, Solna, Sweden（MedTechLabs，BioClinicum，卡罗林斯卡大学医院，索纳，瑞典）
A Two-Scan Deep Learning Model for Predicting Dementia in Mild Cognitive Impairment
organization= School of Business Law；Entrepreneurship, Swinburne University of Technology , city= Melbourne , state= VIC , country= Australia；organization= Australian Regenerative Medicine Institute, Monash University , city= Melbourne , state= VIC , country= Australia；organization= Data Science \& Big Data Lab, Universidad Pablo de Olavide , city= Seville , country= Spain；organization= The Maersk Mc-Kinney M ller Institute, University of Southern Denmark , city= Odense , country= Denmark
