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ABCD-NP@MICCAI 2019: Shenzhen, China
- Kilian M. Pohl, Wesley K. Thompson, Ehsan Adeli, Marius George Linguraru:
Adolescent Brain Cognitive Development Neurocognitive Prediction - First Challenge, ABCD-NP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings. Lecture Notes in Computer Science 11791, Springer 2019, ISBN 978-3-030-31900-7 - Yeeleng Scott Vang, Yingxin Cao, Xiaohui Xie:
A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction. 1-8 - Po-Yu Kao
, Angela Zhang, Michael Goebel, Jefferson W. Chen, B. S. Manjunath:
Predicting Fluid Intelligence of Children Using T1-Weighted MR Images and a StackNet. 9-16 - Luke M. Guerdan, Peng Sun, Connor Rowland, Logan Harrison, Zhicheng Tang, Nickolas M. Wergeles, Yi Shang:
Deep Learning vs. Classical Machine Learning: A Comparison of Methods for Fluid Intelligence Prediction. 17-25 - Michael Rebsamen
Surface-Based Brain Morphometry for the Prediction of Fluid Intelligence in the Neurocognitive Prediction Challenge 2019. 26-34 - Sebastian Pölsterl
Prediction of Fluid Intelligence from T1-Weighted Magnetic Resonance Images. 35-46 - José G. Tamez-Peña
Ensemble of SVM, Random-Forest and the BSWiMS Method to Predict and Describe Structural Associations with Fluid Intelligence Scores from T1-Weighed MRI. 47-56 - Juan Miguel Valverde
Predicting Intelligence Based on Cortical WM/GM Contrast, Cortical Thickness and Volumetry. 57-65 - Huijing Ren, Xuelin Wang, Sheng Wang, Zhengwu Zhang:
Predict Fluid Intelligence of Adolescent Using Ensemble Learning. 66-73 - Shikhar Srivastava
Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble Approach. 74-82 - Agata Wlaszczyk
Predicting Fluid Intelligence from Structural MRI Using Random Forest regression. 83-91 - Yanli Zhang-James
Nu Support Vector Machine in Prediction of Fluid Intelligence Using MRI Data. 92-98 - Sebastian Pölsterl
An AutoML Approach for the Prediction of Fluid Intelligence from MRI-Derived Features. 99-107 - Lihao Liu, Lequan Yu
Predicting Fluid Intelligence from MRI Images with Encoder-Decoder Regularization. 108-113 - Neil P. Oxtoby
ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Residual Fluid Intelligence Scores from Cortical Grey Matter Morphology. 114-123 - Leo Brueggeman
Ensemble Modeling of Neurocognitive Performance Using MRI-Derived Brain Structure Volumes. 124-132 - Ágoston Mihalik
ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Fluid Intelligence Scores from Structural MRI Using Probabilistic Segmentation and Kernel Ridge Regression. 133-142 - Jeffrey N. Chiang
Predicting Fluid Intelligence Using Anatomical Measures Within Functionally Defined Brain Networks. 143-149 - Sara Ranjbar
Sex Differences in Predicting Fluid Intelligence of Adolescent Brain from T1-Weighted MRIs. 150-157 - Marina Pominova, Anna Kuzina, Ekaterina Kondrateva
Ensemble of 3D CNN Regressors with Data Fusion for Fluid Intelligence Prediction. 158-166 - Tengfei Li
Adolescent Fluid Intelligence Prediction from Regional Brain Volumes and Cortical Curvatures Using BlockPC-XGBoost. 167-175 - Yukai Zou, Ikbeom Jang, Timothy G. Reese
Cortical and Subcortical Contributions to Predicting Intelligence Using 3D ConvNets. 176-185
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