Adolescent brain cognitive development neurocognitive prediction : first Challenge, ABCD-NP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings / Kilian M. Pohl, Wesley K. Thompson, Ehsan Adeli, Marius George Linguraru (eds.).
Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction (1st : 2019 : Shenzhen Shi, China); Pohl, Kilian M. editor.; Thompson, Wesley (Of University of California, San Diego), editor.; Adeli, Ehsan, editor.; Linguraru, Marius George, editor.; International Conference on Medical Image Computing and Computer-Assisted Intervention (22nd : 2019 : Shenzhen Shi, China)
2019
RC386.6.M34
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Title Adolescent brain cognitive development neurocognitive prediction : first Challenge, ABCD-NP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings / Kilian M. Pohl, Wesley K. Thompson, Ehsan Adeli, Marius George Linguraru (eds.).
Meeting Name Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction (1st : 2019 : Shenzhen Shi, China)
ISBN 9783030319014 (electronic book)
3030319016 (electronic book)
9783030319007
DOI https://doi.org/10.1007/978-3-030-31901-4
https://doi.org/10.1007/978-3-030-31
Published Cham, Switzerland : Springer, 2019.
Language English
Description 1 online resource (xi, 188 pages) : illustrations.
Item Number 10.1007/978-3-030-31901-4 doi
10.1007/978-3-030-31
Call Number RC386.6.M34
Dewey Decimal Classification 616.8/047548
Summary This book constitutes the refereed proceedings of the First Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction, ABCD-NP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. 29 submissions were carefully reviewed and 24 of them were accepted. Some of the 24 submissions were merged and resulted in the 21 papers that are presented in this book. The papers explore methods for predicting fluid intelligence from T1-weighed MRI of 8669 children (age 9-10 years) recruited by the Adolescent Brain Cognitive Development Study (ABCD) study; the largest long-term study of brain development and child health in the United States to date.
Note International conference proceedings.
Includes author index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed October 15, 2019).
Added Author Pohl, Kilian M. editor.
Thompson, Wesley (Of University of California, San Diego), editor.
Adeli, Ehsan, editor.
Linguraru, Marius George, editor.
Added Meeting Name International Conference on Medical Image Computing and Computer-Assisted Intervention (22nd : 2019 : Shenzhen Shi, China)
Series Lecture notes in computer science ; 11791.
LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics.
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A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction
Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet
Deep Learning vs. Classical Machine Learning: A Comparison of Methods for Fluid Intelligence Prediction
Surface-based Brain Morphometry for the Prediction of Fluid Intelligence in the Neurocognitive Prediction Challenge 2019
Prediction of Fluid Intelligence From T1-Weighted Magnetic Resonance Images
Ensemble of SVM, Random-Forest and the BSWiMS Method to Predict and Describe Structural Associations with Fluid Intelligence Scores from T1-Weighed MRI
Predicting intelligence based on cortical WM/GM contrast, cortical thickness and volumetry
Predict Fluid Intelligence of Adolescent Using Ensemble Learning
Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble Approach
Predicting Fluid intelligence from structural MRI using Random Forest regression
Nu Support Vector Machine in Prediction of Fluid Intelligence Using MRI Data
An AutoML Approach for the Prediction of Fluid Intelligence From MRI-Derived Features
Predicting Fluid Intelligence from MRI images with Encoder-decoder Regularization
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology
Ensemble Modeling of Neurocognitive Performance Using MRI-derived Brain Structure Volumes
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression
Predicting fluid intelligence using anatomical measures within functionally defined brain networks
Sex differences in predicting fluid intelligence of adolescent brain from T1-weighted MRIs
Ensemble of 3D CNN regressors with data fusion for fluid intelligence prediction
Adolescent fluid intelligence prediction from regional brain volumes and cortical curvatures using BlockPC-XGBoost
Cortical and Subcortical Contributions to Predicting Intelligence using 3D ConvNets.

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