Adolescent Brain Cognitive Development Neurocognitive Prediction : First Challenge, ABCD-NP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings / edited by Kilian M. Pohl, Wesley K. Thompson, Ehsan Adeli, Marius George Linguraru.

Format:

Book

Contributor:

Pohl, Kilian M., editor.

Thompson, Wesley (Of University of California, San Diego), editor.

Adeli, Ehsan, editor.

Linguraru, Marius George, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 11791.

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11791

Language:

English

Subjects (All):

Optical data processing.

Machine learning.

Mathematical statistics.

Data mining.

Image Processing and Computer Vision.

Machine Learning.

Probability and Statistics in Computer Science.

Data Mining and Knowledge Discovery.

Local Subjects:

Image Processing and Computer Vision.

Machine Learning.

Probability and Statistics in Computer Science.

Data Mining and Knowledge Discovery.

Physical Description:

1 online resource (XI, 188 pages) : 57 illustrations, 49 illustrations in color.

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-030-31901-4

9783030319014

Access Restriction:

Restricted for use by site license.