Adolescent Brain Cognitive Development Neurocognitive Prediction: First Challenge, Abcd-NP 2019, Held in Conjunction with Miccai 2019, Shenzhen, China

ISBN-13: 9783030319007 / Angielski / Miękka / 2019 / 188 str.

Kilian M. Pohl; Wesley K. Thompson; Ehsan Adeli

Spis treści

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.