  Translating neural networks from theory to clinical practice has unique
challenges, specifically in the field of neuroimaging. In this paper, we
present DeepNeuro, a deep learning framework that is best-suited to putting
deep learning algorithms for neuroimaging in practical usage with a minimum of
friction. We show how this framework can be used to both design and train
neural network architectures, as well as modify state-of-the-art architectures
in a flexible and intuitive way. We display the pre- and postprocessing
functions common in the medical imaging community that DeepNeuro offers to
ensure consistent performance of networks across variable users, institutions,
and scanners. And we show how pipelines created in DeepNeuro can be concisely
packaged into shareable Docker containers and command-line interfaces using
DeepNeuro's pipeline resources.
