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Titlebook: Connectomics in NeuroImaging; Third International Markus D. Schirmer,Archana Venkataraman,Ai Wern Ch Conference proceedings 2019 Springer

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书目名称Connectomics in NeuroImaging
副标题Third International
编辑Markus D. Schirmer,Archana Venkataraman,Ai Wern Ch
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Connectomics in NeuroImaging; Third International  Markus D. Schirmer,Archana Venkataraman,Ai Wern Ch Conference proceedings 2019 Springer
描述This book constitutes the refereed proceedings of the Third International Workshop on Connectomics in NeuroImaging, CNI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019..The 13 full papers presented were carefully reviewed and selected from 14 submissions. The papers deal with new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis and group comparison studies as well as in various neuroimaging applications..
出版日期Conference proceedings 2019
关键词artificial intelligence; brain connectivity; classification; data mining; diffusion MRI; feature selectio
版次1
doihttps://doi.org/10.1007/978-3-030-32391-2
isbn_softcover978-3-030-32390-5
isbn_ebook978-3-030-32391-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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Covariance Shrinkage for Dynamic Functional Connectivity,nd thoughts. Despite the recent advances in statistical methods, estimating the high dimensional dFC states from a small number of available time points remains a challenge. This paper shows that the challenge is reduced by ., a statistical method used for the estimation of large covariance matrices
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Rapid Acceleration of the Permutation Test via Transpositions,y possible permutation for large-scale brain imaging datasets such as HCP and ADNI with hundreds of subjects is not practical. Many previous attempts at speeding up the permutation test rely on various approximation strategies such as estimating the tail distribution with known parametric distributi
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A Mass Multivariate Edge-wise Approach for Combining Multiple Connectomes to Improve the Detection nces. While these connectomes have traditionally been constructed using resting-state data, recent work has highlighted the importance of combining multiple task connectomes, particularly for identifying individual differences. Yet, these methods have not yet been extended to investigate differences
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Adversarial Connectome Embedding for Mild Cognitive Impairment Identification Using Cortical Morphoques, they can further be utilized to build computer-aided MCI diagnosis models. In this paper, we introduce . (ACE) architecture, which is rooted in graph convolution and adversarial regularization to learn relevant connectional features for MCI classification. Existing connectome-based embedding m
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