{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deepneuro-an-open-source-deep-learning","title":"DeepNeuro: an open-source deep learning toolbox for neuroimaging","arxiv_id":"1808.04589","date":"2018-08-14","proceeding":null,"authors":["Andrew Beers","James Brown","Ken Chang","Katharina Hoebel","Elizabeth Gerstner","Bruce Rosen","Jayashree Kalpathy-Cramer"],"abstract":"Translating neural networks from theory to clinical practice has unique\nchallenges, specifically in the field of neuroimaging. In this paper, we\npresent DeepNeuro, a deep learning framework that is best-suited to putting\ndeep learning algorithms for neuroimaging in practical usage with a minimum of\nfriction. We show how this framework can be used to both design and train\nneural network architectures, as well as modify state-of-the-art architectures\nin a flexible and intuitive way. We display the pre- and postprocessing\nfunctions common in the medical imaging community that DeepNeuro offers to\nensure consistent performance of networks across variable users, institutions,\nand scanners. And we show how pipelines created in DeepNeuro can be concisely\npackaged into shareable Docker containers and command-line interfaces using\nDeepNeuro's pipeline resources.","url_abs":"http://arxiv.org/abs/1808.04589v1","url_pdf":"http://arxiv.org/pdf/1808.04589v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deepneuro-an-open-source-deep-learning","repo_url":"https://github.com/QTIM-Lab/DeepNeuro","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"deepneuro-an-open-source-deep-learning","repo_url":"https://github.com/QTIM-Lab/DeepRad","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"friction","task_name":"Friction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}