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000915832 019__ $$a1126000162
000915832 020__ $$a9783030319014$$q(electronic book)
000915832 020__ $$a3030319016$$q(electronic book)
000915832 020__ $$z9783030319007
000915832 0247_ $$a10.1007/978-3-030-31901-4$$2doi
000915832 0247_ $$a10.1007/978-3-030-31
000915832 035__ $$aSP(OCoLC)on1123174678
000915832 035__ $$aSP(OCoLC)1123174678$$z(OCoLC)1126000162
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000915832 049__ $$aISEA
000915832 050_4 $$aRC386.6.M34
000915832 08204 $$a616.8/047548$$223
000915832 1112_ $$aChallenge in Adolescent Brain Cognitive Development Neurocognitive Prediction$$n(1st :$$d2019 :$$cShenzhen Shi, China)
000915832 24510 $$aAdolescent brain cognitive development neurocognitive prediction :$$bfirst Challenge, ABCD-NP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings /$$cKilian M. Pohl, Wesley K. Thompson, Ehsan Adeli, Marius George Linguraru (eds.).
000915832 2463_ $$aABCD-NP 2019
000915832 264_1 $$aCham, Switzerland :$$bSpringer,$$c2019.
000915832 300__ $$a1 online resource (xi, 188 pages) :$$billustrations.
000915832 336__ $$atext$$btxt$$2rdacontent
000915832 337__ $$acomputer$$bc$$2rdamedia
000915832 338__ $$aonline resource$$bcr$$2rdacarrier
000915832 4901_ $$aLecture notes in computer science ;$$v11791
000915832 4901_ $$aLNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics
000915832 500__ $$aInternational conference proceedings.
000915832 500__ $$aIncludes author index.
000915832 5050_ $$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.
000915832 506__ $$aAccess limited to authorized users.
000915832 520__ $$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.
000915832 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 15, 2019).
000915832 650_0 $$aBrain$$xMagnetic resonance imaging$$vCongresses.
000915832 650_0 $$aBrain$$xGrowth$$vCongresses.
000915832 7001_ $$aPohl, Kilian M.$$eeditor.
000915832 7001_ $$aThompson, Wesley$$c(Of University of California, San Diego),$$eeditor.
000915832 7001_ $$aAdeli, Ehsan,$$eeditor.
000915832 7001_ $$aLinguraru, Marius George,$$eeditor.
000915832 7112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(22nd :$$d2019 :$$cShenzhen Shi, China)
000915832 830_0 $$aLecture notes in computer science ;$$v11791.
000915832 830_0 $$aLNCS sublibrary.$$nSL 6,$$pImage processing, computer vision, pattern recognition, and graphics.
000915832 852__ $$bebk
000915832 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-31901-4$$zOnline Access$$91397441.1
000915832 909CO $$ooai:library.usi.edu:915832$$pGLOBAL_SET
000915832 980__ $$aEBOOK
000915832 980__ $$aBIB
000915832 982__ $$aEbook
000915832 983__ $$aOnline
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