| Literature DB >> 32578020 |
Andrew Beers1, James Brown1, Ken Chang1, Katharina Hoebel1, Jay Patel1, K Ina Ly1,2, Sara M Tolaney3, Priscilla Brastianos2, Bruce Rosen1, Elizabeth R Gerstner1,2, Jayashree Kalpathy-Cramer4.
Abstract
Translating deep learning research from theory into clinical practice has unique challenges, specifically in the field of neuroimaging. In this paper, we present DeepNeuro, a Python-based deep learning framework that puts deep neural networks for neuroimaging into practical usage with a minimum of friction during implementation. We show how this framework can be used to design deep learning pipelines that can load and preprocess data, design and train various neural network architectures, and evaluate and visualize the results of trained networks on evaluation data. We present a way of reproducibly packaging data pre- and postprocessing functions common in the neuroimaging community, which facilitates consistent performance of networks across variable users, institutions, and scanners. We show how deep learning pipelines created with DeepNeuro can be concisely packaged into shareable Docker and Singularity containers with user-friendly command-line interfaces.Entities:
Keywords: Augmentation; Deep learning; Docker; Neuroimaging; Preprocessing
Mesh:
Year: 2021 PMID: 32578020 PMCID: PMC7786286 DOI: 10.1007/s12021-020-09477-5
Source DB: PubMed Journal: Neuroinformatics ISSN: 1539-2791