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020 _a9783030323912
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024 7 _a10.1007/978-3-030-32391-2
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
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072 7 _aCOM004000
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245 1 0 _aConnectomics in NeuroImaging
_h[electronic resource] :
_bThird International Workshop, CNI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings /
_cedited by Markus D. Schirmer, Archana Venkataraman, Islem Rekik, Minjeong Kim, Ai Wern Chung.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aX, 139 p. 53 illus., 51 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
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490 1 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v11848
505 0 _aUnsupervised Feature Selection via Adaptive Embedding and Sparse Learning for Parkinson's Disease Diagnosis -- A Novel Graph Neural Network to Localize Eloquent Cortex in Brain Tumor Patients from Resting-State fMRI Connectivity -- Graph Morphology-Based Genetic Algorithm for Classifying Late Dementia States -- Covariance Shrinkage for Dynamic Functional Connectivity -- Rapid Acceleration of the Permutation Test via Transpositions -- Heat kernels with functional connectomes reveal atypical energy transport in peripheral subnetworks in autism -- A Mass Multivariate Edge-wise Approach for Combining Multiple Connectomes to Improve the Detection of Group Differences -- Adversarial Connectome Embedding for Mild Cognitive Impairment Identification using Cortical Morphological Networks -- A Machine Learning Framework for Accurate Functional Connectome Fingerprinting and an Application of a Siamese Network -- Test-Retest Reliability of Functional Networks for Evaluation of Data-Driven Parcellation.-Constraining Disease Progression Models Using Subject Specific Connectivity Priors -- Hemodynamic Matrix Factorization for Functional Magnetic Resonance Imaging -- Network Dependency Index Stratified Subnetwork Analysis of Functional Connectomes: An application to autism.
520 _aThis 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.
650 0 _aArtificial intelligence.
_93407
650 0 _aData mining.
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650 0 _aComputer science.
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650 0 _aImage processing
_xDigital techniques.
_94145
650 0 _aComputer vision.
_984835
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aData Mining and Knowledge Discovery.
_984836
650 2 4 _aModels of Computation.
_931806
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_931569
700 1 _aSchirmer, Markus D.
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700 1 _aVenkataraman, Archana.
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700 1 _aRekik, Islem.
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700 1 _aKim, Minjeong.
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700 1 _aChung, Ai Wern.
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710 2 _aSpringerLink (Online service)
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773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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830 0 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
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_984849
856 4 0 _uhttps://doi.org/10.1007/978-3-030-32391-2
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