Align-cDAE: Alzheimer's Disease Progression Modeling with Attention-Aligned Conditional Diffusion Auto-Encoder
Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India（电子工程系，印度理工学院马德拉斯，钦奈，印度）；Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, India（苏达·戈帕拉克里希南脑中心，印度理工学院马德拉斯，钦奈，印度）；Department of Electrical Engineering, Indian Institute of Technology Madras Chennai, India（电子工程系，印度理工学院马德拉斯钦奈，印度）
Generative AI framework-based modeling and prediction of longitudinal human brain images offer an efficient mechanism to track neurodegenerative progression, essential for the assessment of diseases like Alzheimer's. Among the existing generative approaches, recent diffusion-based models have emerged as an effective alternative to generate disease progression images. Incorporating multi-modal and non-imaging attributes as conditional information into diffusion frameworks has been shown to improve controllability during such generations. However, existing methods do not explicitly ensure that information from non-imaging conditioning modalities is meaningfully aligned with image features to introduce desirable changes in the generated images, such as modulation of progression-specific regions. Further, more precise control over the generation process can be achieved by introducing progression-relevant structure into the internal representations of the model, lacking in the existing approaches. To address these limitations, we propose a diffusion autoencoder-based framework for disease progression modeling that explicitly enforces alignment between different modalities. The alignment is enforced by introducing an explicit objective function that enables the model to focus on the regions exhibiting progression-related changes. Further, we devise a mechanism to better structure the latent representational space of the diffusion auto-encoding framework. Specifically, we assign separate latent subspaces for integrating progression-related conditions and retaining subject-specific identity information, allowing better-controlled image generation. These results demonstrate that enforcing alignment and better structuring of the latent representational space of diffusion auto-encoding framework leads to more anatomically precise modeling of Alzheimer's disease progression.
HIBMatch: Hypergraph Information Bottleneck for Semi-supervised Alzheimer's Progression
Department of Applied Mathematics and Theoretical Physics, University of Cambridge（应用数学与理论物理系，剑桥大学）；Department of Biomedical Engineering, The Hong Kong Polytechnic University（生物医学工程系，香港理工大学）；Research Institute for Artificial Intelligence of Things, The Hong Kong Polytechnic University（物联网人工智能研究所，香港理工大学）；Yau Mathematical Sciences Centre, Tsinghua University（叶德平数学科学中心，清华大学）
Early Prediction of Alzheimer's Disease Dementia Based on Baseline Hippocampal MRI and 1-Year Follow-Up Cognitive Measures Using Deep Recurrent Neural Networks
Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）
Alzheimers Disease Progression Prediction Based on Manifold Mapping of Irregularly Sampled Longitudinal Data
College of Computer Science and Technology, Huaqiao University（华侨大学计算机科学与技术学院）；Key Laboratory of Computer Vision and Machine Learning in Fujian Province（福建省计算机视觉与机器学习重点实验室）；College of Information Science and Engineering,Ritsumeikan University（立命馆大学信息科学与工程学院）
Graph Theory and GNNs to Unravel the Topographical Organization of Brain Lesions in Variants of Alzheimer's Disease Progression
Sorbonne Université（索邦大学）；Paris Brain Institute - ICM（巴黎脑研究所 - 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）
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）
Deformation-aware Temporal Generation for Early Prediction of Alzheimers Disease
the College of Computer Science and Technology,Huaqiao University（华侨大学计算机科学与技术学院）；the Key Laboratory of Computer Vision and Machine Learning in Fujian Province（福建省计算机视觉与机器学习重点实验室）
Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen University（深圳大学）；Renmin University of China（中国人民大学）；Harbin Engineering University（哈尔滨工程大学）
Télécom SudParis, Institut Polytechnique de Paris（巴黎电信 SudParis，巴黎综合理工学院）；University College London（伦敦大学学院）；Digital Technologies and Artificial Intelligence Development Research Institute (AIRI)（数字技术与人工智能发展研究院（AIRI））
A Machine Learning Approach to Analyze the Effects of Alzheimer's Disease on Handwriting through Lognormal Features
University of Cassino and Southern Lazio（卡西诺和南拉齐奥大学）；IDeTIC: Instituto para el Desarrollo Tecnológico y la Innovación en Comunicaciones（通信技术与创新发展研究所）；Universidad de Las Palmas de Gran Canaria（大加那利岛拉斯帕尔马斯大学）
Enhancing Early Alzheimer Disease Detection through Big Data and Ensemble Few-Shot Learning
Robotics and Internet of Things Laboratory, Prince Sultan University（王子苏大学机器人与物联网实验室）；RIADI Laboratory, National School of Computer Science, University of Manouba（RIADI实验室，国家计算机科学学院，曼努巴大学）
A Prior Guided Adversarial Representation Learning and Hypergraph Perceptual Network for Predicting Abnormal Connections of Alzheimer's Disease
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen University（深圳大学）；Renmin University of China（中国人民大学）；Central South University（中南大学）
Collaborative Attention and Consistent-Guided Fusion of MRI and PET for Alzheimer's Disease Diagnosis
School of Software Yunnan University（软件学院 云南大学）；Department of Computer Science The University of Sheffield（计算机科学系 剑桥大学）；Department of Computing Xian JiaoTong-Liverpool University（计算系 西交利物浦大学）
DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders
University College London（伦敦大学学院）；Inria（法国国家信息与自动化研究所）；Université Côte d'Azur（蔚蓝海岸大学）
在这里，我们提出了DIVE：数据驱动的顶点进化推理。DIVE是一种基于图像的疾病进展模型，具有单顶点分辨率，旨在从短期纵向数据集重建大脑病理学的长期模式。DIVE对在患者群体中具有类似时间动态的皮质表面上的逐顶点生物标志物测量进行聚类，并且同时估计每个聚类中的顶点测量的平均轨迹。DIVE独特地将皮质分割成具有共同进展模式的区域，从而为个体疾病提供新的签名。DIVE进一步估计每个受试者每次访视的疾病分期和进展速度，可能增强临床试验或管理的分层。在模拟数据上，DIVE可以恢复地面实况集群及其底层轨迹，前提是集群之间的平均轨迹有足够的差异。我们证明了DIVE的数据来自两个队列：阿尔茨海默病神经影像学倡议（ADNI）和痴呆症研究中心（DRC），英国，包含患者与后皮质萎缩（PCA）以及典型的阿尔茨海默病（tAD）。DIVE在两个独立的数据集（ADNI和DRC）中发现了tAD受试者的萎缩的相似空间模式，并进一步揭示了不同疾病（tAD vs PCA）和不同类型生物标志物数据的不同病理模式：来自磁共振成像（MRI）的皮质厚度vs来自正电子发射断层扫描（PET）的淀粉样蛋白负荷。最后，DIVE可用于使用任何种类的逐体素或逐顶点测量（包括雅可比压缩图、来自扩散成像的分数各向异性（FA）图或其他PET测量）来估计脑中病理的细粒度空间分布。DIVE源代码可在线获得：https://github.com/m…
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）
Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI Classification in Alzheimer Diagnosis
Huacong Qingjiao Information Technology (Beijing) Co., Ltd.（华聪青教信息技术（北京）有限公司）；Chongqing Technology and Business University（重庆工商大学）；University of Maine at Presque Isle（缅因大学普雷斯克艾尔分校）；San Francisco Bay University（旧金山湾大学）；Universitario Tecnológico Universitam；Northern Arizona University（北亚利桑那大学）
