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ORCIDABCD-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, Christian Rummel, Ines Mürner-Lavanchy, Mauricio Reyes, Roland Wiest, Richard McKinley:
Surface-Based Brain Morphometry for the Prediction of Fluid Intelligence in the Neurocognitive Prediction Challenge 2019. 26-34 - Sebastian Pölsterl, Benjamín Gutiérrez-Becker, Ignacio Sarasua, Abhijit Guha Roy, Christian Wachinger:
Prediction of Fluid Intelligence from T1-Weighted Magnetic Resonance Images. 35-46 - José G. Tamez-Peña, Jorge Orozco, Patricia Sosa, Alejandro Valdes, Fahimeh Nezhadmoghadam:
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, Vandad Imani, John D. Lewis, Jussi Tohka:
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, Fabian Eitel, Kerstin Ritter:
Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble Approach. 74-82 - Agata Wlaszczyk, Agnieszka Kaminska, Agnieszka Pietraszek, Jakub Dabrowski, Mikolaj A. Pawlak, Hanna Nowicka:
Predicting Fluid Intelligence from Structural MRI Using Random Forest regression. 83-91 - Yanli Zhang-James, Stephen J. Glatt, Stephen V. Faraone:
Nu Support Vector Machine in Prediction of Fluid Intelligence Using MRI Data. 92-98 - Sebastian Pölsterl, Benjamín Gutiérrez-Becker, Ignacio Sarasua, Abhijit Guha Roy, Christian Wachinger:
An AutoML Approach for the Prediction of Fluid Intelligence from MRI-Derived Features. 99-107 - Lihao Liu, Lequan Yu, Shujun Wang, Pheng-Ann Heng:
Predicting Fluid Intelligence from MRI Images with Encoder-Decoder Regularization. 108-113 - Neil P. Oxtoby, Fabio S. Ferreira, Ágoston Mihalik, Tong Wu, Mikael Brudfors, Hongxiang Lin, Anita Rau, Stefano B. Blumberg, Maria Robu, Cemre Zor, Maira Tariq, Mar Estarellas Garcia, Baris Kanber, Daniil I. Nikitichev, Janaina Mourão Miranda:
ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Residual Fluid Intelligence Scores from Cortical Grey Matter Morphology. 114-123 - Leo Brueggeman, Tanner Koomar, Yongchao Huang, Brady Hoskins, Tien Tong, James Kent, Ethan Bahl, Charles E. Johnson, Alexander Powers, Douglas R. Langbehn, Jatin G. Vaidya, Hans J. Johnson, Jacob J. Michaelson:
Ensemble Modeling of Neurocognitive Performance Using MRI-Derived Brain Structure Volumes. 124-132 - Ágoston Mihalik, Mikael Brudfors, Maria Robu, Fabio S. Ferreira, Hongxiang Lin, Anita Rau, Tong Wu, Stefano B. Blumberg, Baris Kanber, Maira Tariq, Mar Estarellas Garcia, Cemre Zor, Daniil I. Nikitichev, Janaina Mourão Miranda, Neil P. Oxtoby:
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, Nicco Reggente, John Dell'Italia, Zhong Sheng Zheng, Evan S. Lutkenhoff:
Predicting Fluid Intelligence Using Anatomical Measures Within Functionally Defined Brain Networks. 143-149 - Sara Ranjbar, Kyle W. Singleton, Lee Curtin, Susan Christine Massey, Andrea Hawkins-Daarud, Pamela R. Jackson, Kristin R. Swanson:
Sex Differences in Predicting Fluid Intelligence of Adolescent Brain from T1-Weighted MRIs. 150-157 - Marina Pominova, Anna Kuzina, Ekaterina Kondrateva, Svetlana Sushchinskaya, Evgeny Burnaev, Vyacheslav Yarkin, Maxim Sharaev:
Ensemble of 3D CNN Regressors with Data Fusion for Fluid Intelligence Prediction. 158-166 - Tengfei Li, Xifeng Wang, Tianyou Luo, Yue Yang, Bingxin Zhao, Liuqing Yang, Ziliang Zhu, Hongtu Zhu:
Adolescent Fluid Intelligence Prediction from Regional Brain Volumes and Cortical Curvatures Using BlockPC-XGBoost. 167-175 - Yukai Zou, Ikbeom Jang, Timothy G. Reese, Jinxia Yao, Wenbin Zhu, Joseph Vincent Rispoli:
Cortical and Subcortical Contributions to Predicting Intelligence Using 3D ConvNets. 176-185
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