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Adolescent brain cognitive development neurocognitive prediction = first Challenge, ABCD-NP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019 : proceedings /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Adolescent brain cognitive development neurocognitive prediction/ edited by Kilian M. Pohl ... [et al.].
其他題名:	first Challenge, ABCD-NP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019 : proceedings /
其他題名:	ABCD-NP 2019
其他作者:	Pohl, Kilian M.
團體作者:	Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	xi, 188 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Brain - Congresses. - Magnetic resonance imaging -
電子資源:	https://doi.org/10.1007/978-3-030-31901-4
ISBN:	9783030319014

Adolescent brain cognitive development neurocognitive prediction = first Challenge, ABCD-NP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019 : proceedings /
Adolescent brain cognitive development neurocognitive predictionfirst Challenge, ABCD-NP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019 : proceedings /[electronic resource] :ABCD-NP 2019edited by Kilian M. Pohl ... [et al.]. - Cham :Springer International Publishing :2019. - xi, 188 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,117910302-9743 ;. - Lecture notes in computer science ;11791..

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.

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.

ISBN: 9783030319014

Standard No.: 10.1007/978-3-030-31901-4doiSubjects--Topical Terms:

3453595
Brain
--Magnetic resonance imaging--Congresses.

LC Class. No.: RC386.6.M34 / C43 2019

Dewey Class. No.: 616.8047548

Adolescent brain cognitive development neurocognitive prediction = first Challenge, ABCD-NP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019 : proceedings /
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