Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeepNeuro: an open-source deep learning toolbox for neuroimaging   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.

10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th