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Single subject prediction of brain disorders from neuroimaging data has gained increasing attention in recent years.
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“Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls”
Mohammad Arbabshirani, Sergey Plis, Jing Sui and Vince Calhoun · 2017
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“Resting-state functional connectivity in autism spectrum disorders: a review”
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Shaojie Bai, J Kolter and Vladlen Koltun · 2018
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“The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features”
Zaixu Cui and Gaolang Gong · 2018
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“Bert: Pre-training of deep bidirectional transformers for language understanding”
Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova · 2018
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Anibalólon Heinsfeld et al · 2018
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“Improving language understanding by generative pre-training”, 2018
Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever · 2018
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Xiaoxiao Li et al · 2021
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“Pre-training and Fine-tuning Transformers for fMRI Prediction Tasks”
Itzik Malkiel, Gony Rosenman, Lior Wolf and Talma Hendler · 2021
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“Deep learning applications for the classification of psychiatric disorders using neuroimaging data: systematic review and meta-analysis”
Mirjam Quaak, Laurens van Mortel, Rajat Thomas and Guido van Wingen · 2021
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“A multi-site, multi-disorder resting-state magnetic resonance image database”
Saori Tanaka et al · 2021
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Haoyi Zhou et al · 2021
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