Multi-modal Graph Neural Network for Early Diagnosis of Alzheimer's Disease from sMRI and PET Scans
College of Electronics and Information Engineering, Sichuan University（四川大学电子信息工程学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）
Yanteng Zhanga, Xiaohai He, Yi Hao Chan, Qizhi Teng, Jagath C. Rajapakse
In recent years, deep learning models have been applied to neuroimaging data for early diagnosis of Alzheimer's disease (AD). Structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) images provide structural and functional information about the brain, respectively. Combining these features leads to improved performance than using a single modality alone in building predictive models for AD diagnosis. However, current multi-modal approaches in deep learning, based on sMRI and PET, are mostly limited to convolutional neural networks, which do not facilitate integration of both image and phenotypic information of subjects. We propose to use graph neural networks (GNN) that are designed to deal with problems in non-Euclidean domains. In this study, we demonstrate how brain networks can be created from sMRI or PET images and be used in a population graph framework that can combine phenotypic information with imaging features of these brain networks. Then, we present a multi-modal GNN framework where each modality has its own branch of GNN and a technique is proposed to combine the multi-modal data at both the level of node vectors and adjacency matrices. Finally, we perform late fusion to combine the preliminary decisions made in each branch and produce a final prediction. As multi-modality data becomes available, multi-source and multi-modal is the trend of AD diagnosis. We conducted explorative experiments based on multi-modal imaging data combined with non-imaging phenotypic information for AD diagnosis and analyzed the impact of phenotypic information on diagnostic performance. Results from experiments demonstrated that our proposed multi-modal approach improves performance for AD diagnosis, and this study also provides technical reference and support the need for multivariate multi-modal diagnosis methods.
sMRI-PatchNet: A novel explainable patch-based deep learning network for Alzheimer's disease diagnosis and discriminative atrophy localisation with Structural MRI
Manchester Metropolitan University（曼彻斯特城市大学）；Brunel University（布鲁内尔大学）；University of Sheffield（谢菲尔德大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
Lab for Artificial Intelligence in Medical Imaging (AI-Med), KJP, LMU Klinikum（慕尼黑大学附属医院儿童与青少年精神病学系医学影像人工智能实验室）；Technical University of Munich, School of Medicine（慕尼黑工业大学医学院）
Alzheimer's Disease (AD) is the most common form of dementia and often difficult to diagnose due to the multifactorial etiology of dementia. Recent works on neuroimaging-based computer-aided diagnosis with deep neural networks (DNNs) showed that fusing structural magnetic resonance images (sMRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) leads to improved accuracy in a study population of healthy controls and subjects with AD. However, this result conflicts with the established clinical knowledge that FDG-PET better captures AD-specific pathologies than sMRI. Therefore, we p…
Diagnosing Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its innate traits of irreversibility with subtle and gradual progression. These characteristics make AD biomarker identification from structural brain imaging (e.g., structural MRI) scans quite challenging. Furthermore, there is a high possibility of getting entangled with normal aging. We propose a novel deep-learning approach through eXplainable AD Likelihood Map Estimation (XADLiME) for AD progression modeling over 3D sMRIs using clinically-guided prototype learning. Specifically, we establish a set of top…
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…
Institute of Smart Systems and Artificial Intelligence, Nazarbayev University（纳扎尔巴耶夫大学智能系统与人工智能研究所）
Alzheimer's disease (AD) is a progressive brain disorder that causes memory and functional impairments. The advances in machine learning and publicly available medical datasets initiated multiple studies in AD diagnosis. In this work, we utilize a multi-modal deep learning approach in classifying normal cognition, mild cognitive impairment and AD classes on the basis of structural MRI and diffusion tensor imaging (DTI) scans from the OASIS-3 dataset. In addition to a conventional multi-modal network, we also present an input agnostic architecture that allows diagnosis with either sMRI or DTI s…
University of Science and Technology of China（中国科学技术大学）；The Ohio State University（俄亥俄州立大学）
Early and accurate diagnosis of Alzheimer's disease (AD) and its prodromal period mild cognitive impairment (MCI) is essential for the delayed disease progression and the improved quality of patients'life. The emerging computer-aided diagnostic methods that combine deep learning with structural magnetic resonance imaging (sMRI) have achieved encouraging results, but some of them are limit of issues such as data leakage and unexplainable diagnosis. In this research, we propose a novel end-to-end deep learning approach for automated diagnosis of AD and localization of important brain regions rel…
Shanghai University（上海大学）；Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences（中国科学院苏州生物医学工程技术研究所）；University of Science and Technology of China（中国科学技术大学）；Nanjing Medical University（南京医科大学）；The University of Electronic Science and Technology of China（电子科技大学）
FDG-PET reveals altered brain metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Some biomarkers derived from FDG-PET by computer-aided-diagnosis (CAD) technologies have been proved that they can accurately diagnosis normal control (NC), MCI, and AD. However, the studies of identification of early MCI (EMCI) and late MCI (LMCI) with FDG-PET images are still insufficient. Compared with studies based on fMRI and DTI images, the researches of the inter-region representation features in FDG-PET images are insufficient. Moreover, considering the variability…
In recent days, Convolutional Neural Networks (CNN) have demonstrated impressive performance in medical image analysis. However, there is a lack of clear understanding of why and how the Convolutional Neural Network performs so well for image analysis task. How CNN analyzes an image and discriminates among samples of different classes are usually considered as non-transparent. As a result, it becomes difficult to apply CNN based approaches in clinical procedures and automated disease diagnosis systems. In this paper, we consider this issue and work on visualizing and understanding the decision…
Positron Emission Tomography (PET) is an important tool for studying Alzheimer's disease (AD). PET scans can be used as diagnostics tools, and to provide molecular characterization of patients with cognitive disorders. However, multiple tracers are needed to measure glucose metabolism (18F-FDG), synaptic vesicle protein (11C-UCB-J), and $β$-amyloid (11C-PiB). Administering multiple tracers to patient will lead to high radiation dose and cost. In addition, access to PET scans using new or less-available tracers with sophisticated production methods and short half-life isotopes may be very limit…
MCR-VQGAN: A Scalable and Cost-Effective Tau PET Synthesis Approach for Alzheimer's Disease Imaging
Department of Biomedical Engineering, Wake Forest University School of Medicine（生物医学工程系，威克森林大学医学院）；Department of Radiology, Wake Forest School of Medicine（放射学系，威克森林医学院）；Department of Radiology and Biomedical Imaging, Yale School of Medicine（放射学与生物医学成像系，耶鲁医学院）
Enhancing Alzheimer's Diagnosis: Leveraging Anatomical Landmarks in Graph Convolutional Neural Networks on Tetrahedral Meshes
School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA（计算与增强智能学院，亚利桑那州立大学）；Amazon AGI, Redmond, WA, USA（亚马逊人工智慧部门）；Banner Alzheimer’s Institute, Phoenix, AZ, USA（贝纳尔德阿尔茨海默病研究所）
GMN4AD: Graph Matching Network for Alzheimer's Disease Diagnosis with Test-Time Domain Adaptation using Multi-centered Structure Magnetic Resonance Imaging
Department of Computer Science, Kennesaw State University（肯纳邦大学计算机科学系）；Department of Information Technology, Kennesaw State University（肯纳邦大学信息技术系）；School of Data Science and Analytics, Kennesaw State University（肯纳邦大学数据科学与分析学院）；Department of Applied Computing, Michigan Technological University（密歇根技术大学应用计算系）
Yuanpei College Peking University（北京大学元培学院）；Massachusetts General Hospital（麻省总医院）；College of Computer Science Zhejiang University（浙江大学计算机学院）
Imaging-based early diagnosis of Alzheimer Disease (AD) has become an effective approach, especially by using nuclear medicine imaging techniques such as Positron Emission Topography (PET). In various literature it has been found that PET images can be better modeled as signals (e.g. uptake of florbetapir) defined on a network (non-Euclidean) structure which is governed by its underlying graph patterns of pathological progression and metabolic connectivity. In order to effectively apply deep learning framework for PET image analysis to overcome its limitation on Euclidean grid, we develop a so…
NVIDIA Corporation（英伟达公司）；Seoul National University（首尔大学）
Positron Emission Tomography (PET) is now regarded as the gold standard for the diagnosis of Alzheimer's Disease (AD). However, PET imaging can be prohibitive in terms of cost and planning, and is also among the imaging techniques with the highest dosage of radiation. Magnetic Resonance Imaging (MRI), in contrast, is more widely available and provides more flexibility when setting the desired image resolution. Unfortunately, the diagnosis of AD using MRI is difficult due to the very subtle physiological differences between healthy and AD subjects visible on MRI. As a result, many attempts have…
Technical University Munich（慕尼黑工业大学）；Ludwig Maximilians University Munich（慕尼黑大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Alzheimer's disease (AD) is the most common cause of dementia. An early detection is crucial for slowing down the disease and mitigating risks related to the progression. While the combination of MRI and FDG-PET is the best image-based tool for diagnosis, FDG-PET is not always available. The reliable detection of Alzheimer's disease with only MRI could be beneficial, especially in regions where FDG-PET might not be affordable for all patients. To this end, we propose a multi-task method based on U-Net that takes T1-weighted MR images as an input to generate synthetic FDG-PET images and classif…
Lehigh University（里海大学）；University of Pennsylvania（宾夕法尼亚大学）
Identification of brain regions related to the specific neurological disorders are of great importance for biomarker and diagnostic studies. In this paper, we propose an interpretable Graph Convolutional Network (GCN) framework for the identification and classification of Alzheimer's disease (AD) using multi-modality brain imaging data. Specifically, we extended the Gradient Class Activation Mapping (Grad-CAM) technique to quantify the most discriminative features identified by GCN from brain connectivity patterns. We then utilized them to find signature regions of interest (ROIs) by detecting…
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）
Flexible Multimodal Neuroimaging Fusion for Alzheimer's Disease Progression Prediction
Department of Computer Science and Engineering（计算机科学与工程系）；Department of Biomedical Informatics（生物医学信息学系）；Department of Neurology（神经病学系）；Translational Data Analytics Institute（转化数据分析研究所）
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）
While Positron emission tomography (PET) imaging has been widely used in diagnosis of number of diseases, it has costly acquisition process which involves radiation exposure to patients. However, magnetic resonance imaging (MRI) is a safer imaging modality that does not involve patient's exposure to radiation. Therefore, a need exists for an efficient and automated PET image generation from MRI data. In this paper, we propose a new frequency-aware attention U-net for generating synthetic PET images. Specifically, we incorporate attention mechanism into different U-net layers responsible for es…
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Shandong University（山东大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）
Positron emission tomography (PET) imaging is an imaging modality for diagnosing a number of neurological diseases. In contrast to Magnetic Resonance Imaging (MRI), PET is costly and involves injecting a radioactive substance into the patient. Motivated by developments in modality transfer in vision, we study the generation of certain types of PET images from MRI data. We derive new flow-based generative models which we show perform well in this small sample size regime (much smaller than dataset sizes available in standard vision tasks). Our formulation, DUAL-GLOW, is based on two invertible …
ADAgent: LLM Agent for Alzheimer's Disease Analysis with Collaborative Coordinator
1 Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China 2 Amazon, USA 3 Shanghai Artificial Intelligence Laboratory, China 4 Department of Geriatrics, Third Affiliated Hospital of Sun Yat-sen University, China 5 Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong SAR, China
针对 AD 分析中多模态、任意模态组合及缺失输入的任务需求，论文提出基于 LLM 的专用智能体 ADAgent，通过推理引擎、医学工具与协作结果协调器提升诊断和预后性能。
Escuela Técnica Superior de Ingenierías Informática y de Telecomunicación（信息与电信工程高等技术学院）
Currently, the diagnosis of Alzheimer's disease is a complex and error-prone process. Improving this diagnosis could allow earlier detection of the disease and improve the quality of life of patients and their families. For this work, we will use 249 brain images from two modalities: PET and MRI, taken from the ADNI database, and labelled into three classes according to the degree of development of Alzheimer's disease. We propose the development of a convolutional neural network to perform the classification of these images, during which, we will study the appropriate depth of the networks for…
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）
Quantitative characterization of disease progression using longitudinal data can provide long-term predictions for the pathological stages of individuals. This work studies the robust modeling of Alzheimer's disease progression using parametric methods. The proposed method linearly maps the individual's age to a disease progression score (DPS) and jointly fits constrained generalized logistic functions to the longitudinal dynamics of biomarkers as functions of the DPS using M-estimation. Robustness of the estimates is quantified using bootstrapping via Monte Carlo resampling, and the estimated…
Brain-Aware Readout Layers in GNNs: Advancing Alzheimer's early Detection and Neuroimaging
AI Graduate School, Gwangju Institute of Science and Technology（光州科学技术院人工智能研究生院）；Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea（韩国天主教大学医学院首尔圣玛丽医院）；Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea（韩国天主教大学医学院汝矣岛圣玛丽医院）
Tau Anomaly Detection in PET Imaging via Bilateral-Guided Deterministic Diffusion Model
Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California（斯蒂文斯神经影像与信息学研究所，凯克医学院，南加州大学）；Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California（明希部门电气与计算机工程系，维特比工程学院，南加州大学）；Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California（阿尔弗雷德·E·曼生物医学工程系，维特比工程学院，南加州大学）
A Surface-based Multimodal Framework for Multitask Analysis in Alzheimer's Disease
Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California (USC)（南加州大学凯克医学院史蒂恩斯神经影像与信息学研究所）；Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California (USC)（南加州大学维特比工程学院阿尔弗雷德·E·曼生物医学工程系）；Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California (USC)（南加州大学维特比工程学院明海电气与计算机工程系）
提出一种增强的球形数据驱动多模态框架，通过球形扩散模型生成配对皮层厚度和Tau PET SUVR数据，结合对比学习和上下文学习，实现多任务分类与回归，在ADNI数据集上优于六种基线模型。
Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET
Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM（大脑研究所 - 巴黎大脑研究所 - ICM）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共医疗集团）；Hôpital de la Pitié Salpêtrière（皮蒂-萨尔皮特里耶医院）
针对脑部 FDG PET 无监督异常检测中残差图像方法的局限性，提出受 Z-scores 启发、利用健康人群变异性的新方法，在 ADNI 数据库上证明其能准确识别阿尔茨海默病相关异常。
Modality-Agnostic Style Transfer for Holistic Feature Imputation
Pohang University of Science and Technology（浦项科技大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of North Carolina at Greensboro（北卡罗来纳大学格林斯伯勒分校）
Arizona State University（亚利桑那州立大学）；Banner Alzheimer’s Institute（班纳阿尔茨海默病研究所）；Southeast University（东南大学）；Children’s Hospital Los Angeles（洛杉矶儿童医院）；Mayo Clinic Arizona（梅奥诊所亚利桑那分院）；University of Southern California（南加州大学）
Biomarker-assisted diagnosis and intervention in Alzheimer's disease (AD) may be the key to prevention breakthroughs. One of the hallmarks of AD is the accumulation of tau plaques in the human brain. However, current methods to detect tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (Tau PET). In our previous work, structural MRI-based hippocampal multivariate morphometry statistics (MMS) showed superior performance as an effective neurodegenerative biomarker for preclinical AD and Patch Analysis-based Surface Correntropy-induced Sparse coding and ma…
Hangzhou Dianzi University（杭州电子科技大学）；Yunnan University（云南大学）；Shenzhen Technology University（深圳技术大学）；Shenzhen University（深圳大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Hangzhou Institute of Advanced Technology（杭州高等研究院）
