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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 /

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. Som...

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Corporate Author: SpringerLink (Online service)
Other Authors: Pohl, Kilian M. (Editor, http://id.loc.gov/vocabulary/relators/edt), Thompson, Wesley K. (Editor, http://id.loc.gov/vocabulary/relators/edt), Adeli, Ehsan. (Editor, http://id.loc.gov/vocabulary/relators/edt), Linguraru, Marius George. (Editor, http://id.loc.gov/vocabulary/relators/edt)
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
Edition:1st ed. 2019.
Series:Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11791
Subjects:
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.
Online Access:https://doi.org/10.1007/978-3-030-31901-4
Table of 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.

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