LEADER 05508nam 22006975 450 001 9910349275503321 005 20230810165720.0 010 $a3-030-31901-6 024 7 $a10.1007/978-3-030-31901-4 035 $a(CKB)4100000009522815 035 $a(DE-He213)978-3-030-31901-4 035 $a(MiAaPQ)EBC5967218 035 $a(PPN)254989942 035 $a(EXLCZ)994100000009522815 100 $a20191008d2019 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdolescent Brain Cognitive Development Neurocognitive Prediction $eFirst Challenge, ABCD-NP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings /$fedited by Kilian M. Pohl, Wesley K. Thompson, Ehsan Adeli, Marius George Linguraru 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XI, 188 p. 57 illus., 49 illus. in color.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v11791 300 $aIncludes index. 311 $a3-030-31900-8 327 $aA 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. 330 $aThis book constitutes the refereed proceedings of the First Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction, ABCD-NP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. 29 submissions were carefully reviewed and 24 of them were accepted. Some of the 24 submissions were merged and resulted in the 21 papers that are presented in this book. The papers explore methods for predicting fluid intelligence from T1-weighed MRI of 8669 children (age 9-10 years) recruited by the Adolescent Brain Cognitive Development Study (ABCD) study; the largest long-term study of brain development and child health in the United States to date. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v11791 606 $aComputer vision 606 $aMachine learning 606 $aComputer science$xMathematics 606 $aMathematical statistics 606 $aData mining 606 $aComputer Vision 606 $aMachine Learning 606 $aProbability and Statistics in Computer Science 606 $aData Mining and Knowledge Discovery 615 0$aComputer vision. 615 0$aMachine learning. 615 0$aComputer science$xMathematics. 615 0$aMathematical statistics. 615 0$aData mining. 615 14$aComputer Vision. 615 24$aMachine Learning. 615 24$aProbability and Statistics in Computer Science. 615 24$aData Mining and Knowledge Discovery. 676 $a616.8047548 676 $a616.8047548 702 $aPohl$b Kilian M$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aThompson$b Wesley$c(Of University of California, San Diego)$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aAdeli$b Ehsan$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLinguraru$b Marius George$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910349275503321 996 $aAdolescent Brain Cognitive Development Neurocognitive Prediction$92532657 997 $aUNINA