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CS 230 Final project. Owned by Soham Gadgil, Sun Woo Kang, and Erick Siavichay-Velasco

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CS 230 Final Project

CS 230 Final project for Predicting Biological Age and Sex using SpatialTemporal Graph CNN for functional MRI data. Owned by Soham Gadgil, Sun Woo Kang, and Erick Siavichay-Velasco

Branches

The sex_clf branch has code for sex classification. The age_clf branch has code for binary age classification using cross entropy loss. The age_clf2 branch has code for age regression using MSELoss.

The master branch has code for getting the results of the analysis in a jupyter notebook analysis.ipynb.

For running the model, please refer to the instructions in the submodule ST-GCN from : https://github.com/yysijie/st-gcn

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition, Sijie Yan, Yuanjun Xiong and Dahua Lin, AAAI 2018. [Arxiv Preprint]

References

[1]G. H. Glover, “Overview of functional magnetic resonance imaging,”Neurosurgery clinics ofNorth America, vol. 22, no. 2, pp. 133–vii, Apr 2011, 21435566[pmid]. [Online]. Available:https://www.ncbi.nlm.nih.gov/pubmed/21435566

[2]H. M. Aycheh, J.-K. Seong, J.-H. Shin, D. L. Na, B. Kang, S. W. Seo, and K.-A.Sohn, “Biological brain age prediction using cortical thickness data: A large scale cohortstudy,”Frontiers in Aging Neuroscience, vol. 10, p. 252, 2018. [Online]. Available:https://www.frontiersin.org/article/10.3389/fnagi.2018.00252

[3]J. Zhai and K. Li, “Predicting brain age based on spatial and temporal features of humanbrain functional networks,”Frontiers in human neuroscience, vol. 13, pp. 62–62, Feb 2019,30863296[pmid]. [Online]. Available: https://www.ncbi.nlm.nih.gov/pubmed/30863296

[4]S. Weis, K. R. Patil, F. Hoffstaedter, A. Nostro, B. T. T. Yeo, and S. B. Eickhoff, “SexClassification by Resting State Brain Connectivity,”Cerebral Cortex, 06 2019, bhz129.[Online]. Available: https://doi.org/10.1093/cercor/bhz129

[5]D. V. Essen, K. Ugurbil, E. Auerbach, D. Barch, T. Behrens, R. Bucholz, A. Chang,L. Chen, M. Corbetta, S. Curtiss, S. D. Penna, D. Feinberg, M. Glasser, N. Harel, A. Heath,L. Larson-Prior, D. Marcus, G. Michalareas, S. Moeller, R. Oostenveld, S. Petersen, F. Prior,B. Schlaggar, S. Smith, A. Snyder, J. Xu, and E. Yacoub, “The human connectome project: Adata acquisition perspective,”NeuroImage, vol. 62, no. 4, pp. 2222 – 2231, 2012, connectivity.[Online]. Available: http://www.sciencedirect.com/science/article/pii/S1053811912001954

[6]C. Zhang, C. C. Dougherty, S. A. Baum, T. White, and A. M. Michael, “Functional connectivitypredicts gender: Evidence for gender differences in resting brain connectivity,” Apr 2018.[Online]. Available: https://www.ncbi.nlm.nih.gov/pubmed/29322586

[7]M. N. I. Qureshi, J. Oh, and B. Lee, “3d-cnn based discrimination of schizophrenia usingresting-state fmri,”Artificial Intelligence in Medicine, vol. 98, pp. 10 – 17, 2019. [Online].Available: http://www.sciencedirect.com/science/article/pii/S0933365719301393

[8]S. Yan, Y. Xiong, and D. Lin, “Spatial temporal graph convolutional networks for skeleton-basedaction recognition,” 2018.

[9]W. Zheng, P. Jing, and Q. Xu, “Action recognition based on spatial temporal graphconvolutional networks,” inProceedings of the 3rd International Conference on ComputerScience and Application Engineering, ser. CSAE 2019.New York, NY, USA: ACM, 2019, pp.118:1–118:5. [Online]. Available: http://doi.acm.org/10.1145/3331453.33616516

[10]Y. Li, Z. He, X. Ye, Z. He, and K. Han, “Spatial temporal graph convolutional networks forskeleton-based dynamic hand gesture recognition,”EURASIP Journal on Image and VideoProcessing, vol. 2019, no. 1, 2019.

[11]T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,”2016.

[12] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” 2015.

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