Contrastive self-supervised learning has recently benefited fMRI classification with inductive biases. Its weak label reliance prevents overfitting on small medical datasets and tackles the high intraclass variances. Nonetheless, existing contrastive methods generate resemblant pairs only on pixel-level features of 3D medical images, while the functional connectivity that reveals critical cognitive information is under-explored. Additionally, existing methods predict labels on individual contrastive representation without recognizing neighbouring information in the patient group, whereas interpatient contrast can act as a similarity measure suitable for population-based classification. We hereby proposed contrastive functional connectivity graph learning for population-based fMRI classification. Representations on the functional connectivity graphs are "repelled" for heterogeneous patient pairs meanwhile homogeneous pairs "attract" each other. Then a dynamic population graph that strengthens the connections between similar patients is updated for classification. Experiments on a multi-site dataset ADHD200 validate the superiority of the proposed method on various metrics. We initially visualize the population relationships and exploit potential subtypes.
VarCoNet: A variability-aware self-supervised framework for functional connectome extraction from resting-state fMRI
Department of Biomedical Engineering and Biotechnology, and the Health Engineering Innovation Group (HEIG), Khalifa University（生物医学工程与生物技术系，以及健康工程创新小组（HEIG），卡利法大学）；Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki（电气与计算机工程系，阿尔基洛斯大学）
Robust contrastive learning and nonlinear ICA in the presence of outliers
Future University Hakodate（公立函馆未来大学）；RIKEN AIP（理化学研究所先进智能项目中心）；University College London（伦敦大学学院）；Université Paris-Saclay（巴黎-萨克雷大学）；Inria（法国国家信息与自动化研究所）；CEA（法国原子能和替代能源委员会）；Helsinki Institute for Information Technology HIIT（赫尔辛基信息技术研究所 HIIT）；University of Helsinki（赫尔辛基大学）
On self-supervised multi-modal representation learning: An application to Alzheimer's disease
Georgia Institute of Technology（佐治亚理工学院）；Georgia State University（佐治亚州立大学）；Emory University（埃默里大学）；Center for Translational Research in Neuroimaging and Data Science（神经影像与数据科学转化研究中心）；Mila（米拉）；Université de Montréal（蒙特利尔大学）
Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions
Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；University of Sydney（悉尼大学）；University of New South Wales（新南威尔士大学）；Massachusetts General Hospital（麻省总医院）
A Confounding Factors-Inhibition Adversarial Learning Framework for Multi-site fMRI Mental Disorder Identification
School of Software, Taiyuan University of Technology（太原科技大学软件学院）；School of Computer Science (Data Science), Taiyuan University of Technology（太原科技大学计算机科学（数据科学）学院）；Max Planck Institute for Human Cognitive and Brain Sciences（马克斯·普朗克人类认知与脑科学研究所）；Department of Psychiatry & Behavioral Sciences, Stanford University（斯坦福大学精神病学与行为科学系）；Montreal Neurological Institute, McGill University（蒙特利尔神经科学研究所，麦吉尔大学）
Contrastive Graph Pooling for Explainable Classification of Brain Networks
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）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；School of Computer Science, the University of Auckland（奥克兰大学计算机学院）；Lee Kong Chian School of Medicine, Nanyang Technological University（南洋理工大学李光前医学院）
BrainSCL: Subtype-Guided Contrastive Learning for Brain Disorder Diagnosis
Computer Science and Engineering, Northeastern University, Shenyang, China（东北大学计算机科学与工程学院）；School of Computer Science and Artificial Intelligence, Shandong Normal University, Jinan, China（山东师范大学计算机科学与人工智能学院）；AiShiWeiLai AI Research, Beijing, China（艾世维来人工智能研究所）；Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China（南京医科大学附属南京脑科医院早期干预科）；Amii, University of Alberta, Edmonton, AB, Canada（阿尔伯塔大学Amii）
Data distribution impacts the performance and generalisability of contrastive learning-based foundation models of electrocardiograms
National Heart and Lung Institute, Imperial College London（伦敦帝国理工学院国家心肺研究所）；Chelsea and Westminster Hospital NHS Foundation Trust（切尔西和威斯敏斯特医院NHS基金会信托）；Zhongshan Hospital of Fudan University（复旦大学附属中山医院）；Shanghai Institute of Cardiovascular Diseases（上海市心血管病研究所）；National Clinical Research Centre for Interventional Medicine（国家介入医学临床研究中心）；Uppsala University（乌普萨拉大学）；Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center（范德堡大学医学中心临床与转化研究所）；Center for Digital Genomic Medicine, Department of Medicine, Vanderbilt University Medical Center（范德堡大学医学中心数字基因组医学中心）；Division of Cardiovascular Medicine, Vanderbilt University Medical Center（范德堡大学医学中心心血管内科）
Behavior Score-Embedded Brain Encoder Network for Improved Classification of Alzheimer Disease Using Resting State fMRI
National Tsing Hua University（国立清华大学）；MOST Joint Research Center for AI Technology and All Vista Healthcare（科技部人工智能技术与全视野医疗联合研究中心）；National Health Research Institutes（国家卫生研究院）
准确检测痴呆症发作的能力在疾病的治疗中是重要的。临床上，阿尔茨海默病（AD）和轻度认知障碍（MCI）患者的诊断基于心理测试和脑成像（例如正电子发射断层扫描（PET）和解剖磁共振成像（MRI））的综合评估。在这项工作中，使用两个不同的数据集，我们提出了一个行为分数嵌入编码器网络（BSEN），集成定期管理的心理测试信息到编码过程中表示主题的静息状态fMRI数据的自动分类任务。BSEN基于3D卷积自动编码器结构，具有对比度损失，使用MiniMental State Examination（MMSE）和临床痴呆评级（CDR）的行为评分进行联合优化。我们提出的使用BSEN的分类框架实现了59.44%的总体识别准确率（3类分类：AD，MCI和健康对照），我们进一步提取了健康对照（HC）和AD患者之间最具区分力的区域。
A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis
School of Energy and Electrical Engineering, Chang'an University（长安大学能源与电气工程学院）；Tulane University（杜兰大学）；School of Data Science, University of Virginia（弗吉尼亚大学数据科学学院）；Georgia State University（佐治亚州立大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
