Adolescent brain cognitive development neurocognitive prediction : first Challenge, ABCD-NP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings /: first Challenge, ABCD-NP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings. (2019)
- Record Type:
- Book
- Title:
- Adolescent brain cognitive development neurocognitive prediction : first Challenge, ABCD-NP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings /: first Challenge, ABCD-NP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings. (2019)
- Main Title:
- Adolescent brain cognitive development neurocognitive prediction : first Challenge, ABCD-NP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings
- Other Titles:
- ABCD-NP 2019
- Further Information:
- Note: Kilian M. Pohl, Wesley K. Thompson, Ehsan Adeli, Marius George Linguraru (eds.).
- Editors:
- Pohl, Kilian M
(Of University of California, San Diego), Thompson, Wesley
Adeli, Ehsan
Linguraru, Marius George - Other Names:
- ABCD-NP (Conference), 1st
International Conference on Medical Image Computing and Computer-Assisted Intervention, 22nd - 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 ChallengeA 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. … (more)
- Publisher Details:
- Cham, Switzerland : Springer
- Publication Date:
- 2019
- Extent:
- 1 online resource (xi, 188 pages), illustrations (some color)
- Subjects:
- 616.8/047548
Brain -- Magnetic resonance imaging -- Congresses
Brain -- Growth -- Congresses
Electronic books - Languages:
- English
- ISBNs:
- 9783030319014
3030319016 - Related ISBNs:
- 9783030319007
- Notes:
- Note: Online resource; title from PDF title page (SpringerLink, viewed October 15, 2019).
- Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.464608
- Ingest File:
- 02_607.xml