Deep learning for neuroimaging: A validation study

125Citations
Citations of this article
629Readers
Mendeley users who have this article in their library.

Abstract

Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.

Cite

CITATION STYLE

APA

Plis, S. M., Hjelm, D. R., Slakhutdinov, R., Allen, E. A., Bockholt, H. J., Long, J. D., … Calhoun, V. D. (2014). Deep learning for neuroimaging: A validation study. Frontiers in Neuroscience, (8 JUL). https://doi.org/10.3389/fnins.2014.00229

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free