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Reproducibility of importance extraction methods in neural network based fMRI classification
[article]
2017
bioRxiv
pre-print
Recent advances in machine learning allow faster training, improved performance and increased interpretability of classification techniques. Consequently, their application in neuroscience is rapidly increasing. While classification approaches have proved useful in functional magnetic resonance imaging (fMRI) studies, there are concerns regarding extraction, reproducibility and visualization of brain regions that contribute most significantly to the classification. We addressed these issues
doi:10.1101/197277
fatcat:hke6vd2dfja5rg6z2ruhclafnq