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Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics

View ORCID ProfileGregory Kiar, View ORCID ProfileYohan Chatelain, View ORCID ProfileAli Salari, View ORCID ProfileAlan C. Evans, View ORCID ProfileTristan Glatard
doi: https://doi.org/10.1101/2020.12.16.423084
Gregory Kiar
1Montreal Neurological Institute, McGill University, Montreal, QC, H3A 2B4, Canada
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  • For correspondence: gregory.kiar@childmind.org
Yohan Chatelain
2Department of Computer Science and Computer Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada
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Ali Salari
2Department of Computer Science and Computer Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada
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Alan C. Evans
1Montreal Neurological Institute, McGill University, Montreal, QC, H3A 2B4, Canada
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Tristan Glatard
2Department of Computer Science and Computer Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada
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  • ORCID record for Tristan Glatard
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Posted July 26, 2021.
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Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics
Gregory Kiar, Yohan Chatelain, Ali Salari, Alan C. Evans, Tristan Glatard
bioRxiv 2020.12.16.423084; doi: https://doi.org/10.1101/2020.12.16.423084
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Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics
Gregory Kiar, Yohan Chatelain, Ali Salari, Alan C. Evans, Tristan Glatard
bioRxiv 2020.12.16.423084; doi: https://doi.org/10.1101/2020.12.16.423084

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