Abstract is missing.
- A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence PredictionYeeleng Scott Vang, Yingxin Cao, Xiaohui Xie. 1-8 [doi]
- Predicting Fluid Intelligence of Children Using T1-Weighted MR Images and a StackNetPo-Yu Kao, Angela Zhang, Michael Goebel, Jefferson W. Chen, B. S. Manjunath. 9-16 [doi]
- Deep Learning vs. Classical Machine Learning: A Comparison of Methods for Fluid Intelligence PredictionLuke M. Guerdan, Peng Sun, Connor Rowland, Logan Harrison, Zhicheng Tang, Nickolas M. Wergeles, Yi Shang. 17-25 [doi]
- Surface-Based Brain Morphometry for the Prediction of Fluid Intelligence in the Neurocognitive Prediction Challenge 2019Michael Rebsamen, Christian Rummel, Ines Mürner-Lavanchy, Mauricio Reyes 0001, Roland Wiest, Richard McKinley. 26-34 [doi]
- Prediction of Fluid Intelligence from T1-Weighted Magnetic Resonance ImagesSebastian Pölsterl, Benjamín Gutiérrez-Becker, Ignacio Sarasua, Abhijit Guha Roy, Christian Wachinger. 35-46 [doi]
- Ensemble of SVM, Random-Forest and the BSWiMS Method to Predict and Describe Structural Associations with Fluid Intelligence Scores from T1-Weighed MRIJosé G. Tamez-Peña, Jorge Orozco, Patricia Sosa, Alejandro Valdes, Fahimeh Nezhadmoghadam. 47-56 [doi]
- Predicting Intelligence Based on Cortical WM/GM Contrast, Cortical Thickness and VolumetryJuan Miguel Valverde, Vandad Imani, John D. Lewis, Jussi Tohka. 57-65 [doi]
- Predict Fluid Intelligence of Adolescent Using Ensemble LearningHuijing Ren, Xuelin Wang, Sheng Wang, Zhengwu Zhang. 66-73 [doi]
- Predicting Fluid Intelligence in Adolescent Brain MRI Data: An Ensemble ApproachShikhar Srivastava, Fabian Eitel, Kerstin Ritter. 74-82 [doi]
- Predicting Fluid Intelligence from Structural MRI Using Random Forest regressionAgata Wlaszczyk, Agnieszka Kaminska, Agnieszka Pietraszek, Jakub Dabrowski, Mikolaj A. Pawlak, Hanna Nowicka. 83-91 [doi]
- Nu Support Vector Machine in Prediction of Fluid Intelligence Using MRI DataYanli Zhang-James, Stephen J. Glatt, Stephen V. Faraone. 92-98 [doi]
- An AutoML Approach for the Prediction of Fluid Intelligence from MRI-Derived FeaturesSebastian Pölsterl, Benjamín Gutiérrez-Becker, Ignacio Sarasua, Abhijit Guha Roy, Christian Wachinger. 99-107 [doi]
- Predicting Fluid Intelligence from MRI Images with Encoder-Decoder RegularizationLihao Liu, Lequan Yu, Shujun Wang, Pheng-Ann Heng. 108-113 [doi]
- ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Residual Fluid Intelligence Scores from Cortical Grey Matter MorphologyNeil 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. 114-123 [doi]
- Ensemble Modeling of Neurocognitive Performance Using MRI-Derived Brain Structure VolumesLeo Brueggeman, Tanner Koomar, Yongchao Huang, Brady Hoskins, Tien Tong, James Kent, Ethan Bahl, Charles E. Johnson, Alexander Powers, Douglas Langbehn, Jatin G. Vaidya, Hans J. Johnson, Jacob J. Michaelson. 124-132 [doi]
- ABCD Neurocognitive Prediction Challenge 2019: Predicting Individual Fluid Intelligence Scores from Structural MRI Using Probabilistic Segmentation and Kernel Ridge RegressionÁgoston 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. 133-142 [doi]
- Predicting Fluid Intelligence Using Anatomical Measures Within Functionally Defined Brain NetworksJeffrey N. Chiang, Nicco Reggente, John Dell'Italia, Zhong Sheng Zheng, Evan S. Lutkenhoff. 143-149 [doi]
- Sex Differences in Predicting Fluid Intelligence of Adolescent Brain from T1-Weighted MRIsSara Ranjbar, Kyle W. Singleton, Lee Curtin, Susan Christine Massey, Andrea Hawkins-Daarud, Pamela R. Jackson, Kristin R. Swanson. 150-157 [doi]
- Ensemble of 3D CNN Regressors with Data Fusion for Fluid Intelligence PredictionMarina Pominova, Anna Kuzina, Ekaterina Kondrateva, Svetlana Sushchinskaya, Evgeny Burnaev, Vyacheslav Yarkin, Maxim Sharaev. 158-166 [doi]
- Adolescent Fluid Intelligence Prediction from Regional Brain Volumes and Cortical Curvatures Using BlockPC-XGBoostTengfei Li, Xifeng Wang, Tianyou Luo, Yue Yang, Bingxin Zhao, Liuqing Yang, Ziliang Zhu, Hongtu Zhu. 167-175 [doi]
- Cortical and Subcortical Contributions to Predicting Intelligence Using 3D ConvNetsYukai Zou, Ikbeom Jang, Timothy G. Reese, Jinxia Yao, Wenbin Zhu, Joseph Vincent Rispoli. 176-185 [doi]