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Given the wide success of convolutional neural networks (CNNs) applied to natural images, researchers have begun to apply them to neuroimaging data.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation
P. Shaw, K. Eckstrand, W. Sharp, J. Blumenthal, J. P. Lerch, D. Greenstein, L. Clasen, A. Evans, J. Giedd, and J. L. Rapoport · 2007
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Prediction of individual brain maturity using fmri
Nico UF Dosenbach, Binyam Nardos, Alexander L Cohen, Damien A Fair, Jonathan D Power, Jessica A Church, Steven M Nelson, Gagan S Wig, Alecia C Vogel, Christina N Lessov-Schlaggar, et al · 2010
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Tiled convolutional neural networks
Jiquan Ngiam, Zhenghao Chen, Daniel Chia, Pang W Koh, Quoc V Le, and Andrew Y Ng · 2010
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Brain maturation: predicting individual brainage in children and adolescents using structural mri
Katja Franke, Eileen Luders, Arne May, Marko Wilke, and Christian Gaser · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Cognitive reserve in ageing and alzheimer’s disease
Yaakov Stern · 2012
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cudnn: Efficient primitives for deep learning
Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer · 2014
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Deep symmetry networks
Robert Gens and Pedro M Domingos · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Neuroimaging of the philadelphia neurodevelopmental cohort
Theodore D Satterthwaite, Mark A Elliott, Kosha Ruparel, James Loughead, Karthik Prabhakaran, Monica E Calkins, Ryan Hopson, Chad Jackson, Jack Keefe, Marisa Riley, et al · 2014
Cited alongside, same era.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, et al · 2015
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Cited alongside, same era.
3d deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients
Dong Nie, Han Zhang, Ehsan Adeli, Luyan Liu, and Dinggang Shen · 2016
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Deepad: Alzheimer′ s disease classification via deep convolutional neural networks using mri and fmri
Saman Sarraf, Ghassem Tofighi, et al · 2016
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Bootstrapping graph convolutional neural networks for autism spectrum disorder classification
Rushil Anirudh and Jayaraman J Thiagarajan · 2017
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Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review
Jose Bernal, Kaisar Kushibar, Daniel S Asfaw, Sergi Valverde, Arnau Oliver, Robert Martí, and Xavier Lladó · 2017
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Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker
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Mental disease feature extraction with mri by 3d convolutional neural network with multi-channel input
Lijun Cao, Zhi Liu, Xiaofu He, Yankun Cao, and Kening Li · 2016
Cited alongside, same era.
Xception: Deep learning with depthwise separable convolutions
François Chollet · 2016
Cited alongside, same era.
Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
Cited alongside, same era.
Exploiting cyclic symmetry in convolutional neural networks
Sander Dieleman, Jeffrey De Fauw, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
James H Cole, Rudra PK Poudel, Dimosthenis Tsagkrasoulis, Matthan WA Caan, Claire Steves, Tim D Spector, and Giovanni Montana · 2017
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A deep cnn based multi-class classification of alzheimer’s disease using mri
Ammarah Farooq, SyedMuhammad Anwar, Muhammad Awais, and Saad Rehman · 2017
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Brainnetcnn: convolutional neural networks for brain networks; towards predicting neurodevelopment
Jeremy Kawahara, Colin J Brown, Steven P Miller, Brian G Booth, Vann Chau, Ruth E Grunau, Jill G Zwicker, and Ghassan Hamarneh · 2017
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Resting state fmri functional connectivity-based classification using a convolutional neural network architecture
Regina J Meszlényi, Krisztian Buza, and Zoltán Vidnyánszky · 2017
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A novel ensemble approach on regionalized neural networks for brain disorder prediction
Lei Zheng, Jingyuan Zhang, Bokai Cao, Philip S Yu, and Ann Ragin · 2017
Later among the works it cites.