000 05508nam a22006135i 4500
001 978-3-030-31901-4
003 DE-He213
005 20240730200430.0
007 cr nn 008mamaa
008 191008s2019 sz | s |||| 0|eng d
020 _a9783030319014
_9978-3-030-31901-4
024 7 _a10.1007/978-3-030-31901-4
_2doi
050 4 _aTA1634
072 7 _aUYQV
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYQV
_2thema
082 0 4 _a006.37
_223
245 1 0 _aAdolescent Brain Cognitive Development Neurocognitive Prediction
_h[electronic resource] :
_bFirst Challenge, ABCD-NP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings /
_cedited by Kilian M. Pohl, Wesley K. Thompson, Ehsan Adeli, Marius George Linguraru.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXI, 188 p. 57 illus., 49 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v11791
505 0 _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 Machinein 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.
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.
650 0 _aComputer vision.
_9163300
650 0 _aMachine learning.
_91831
650 0 _aComputer science
_xMathematics.
_93866
650 0 _aMathematical statistics.
_99597
650 0 _aData mining.
_93907
650 1 4 _aComputer Vision.
_9163301
650 2 4 _aMachine Learning.
_91831
650 2 4 _aProbability and Statistics in Computer Science.
_931857
650 2 4 _aData Mining and Knowledge Discovery.
_9163302
700 1 _aPohl, Kilian M.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9163303
700 1 _aThompson, Wesley K.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9163304
700 1 _aAdeli, Ehsan.
_eeditor.
_0(orcid)
_10000-0002-0579-7763
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9163305
700 1 _aLinguraru, Marius George.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9163306
710 2 _aSpringerLink (Online service)
_9163307
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030319007
776 0 8 _iPrinted edition:
_z9783030319021
830 0 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v11791
_9163308
856 4 0 _uhttps://doi.org/10.1007/978-3-030-31901-4
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
942 _cELN
999 _c96049
_d96049