{"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/delimit-pytorch-an-extension-for-deep","title":"DELIMIT PyTorch - An extension for Deep Learning in Diffusion Imaging","arxiv_id":"1808.01517","date":"2018-08-04","proceeding":null,"authors":["Simon Koppers","Dorit Merhof"],"abstract":"DELIMIT is a framework extension for deep learning in diffusion imaging,\nwhich extends the basic framework PyTorch towards spherical signals. Based on\nseveral novel layers, deep learning can be applied to spherical diffusion\nimaging data in a very convenient way. First, two spherical harmonic\ninterpolation layers are added to the extension, which allow to transform the\nsignal from spherical surface space into the spherical harmonic space, and vice\nversa. In addition, a local spherical convolution layer is introduced that adds\nthe possibility to include gradient neighborhood information within the\nnetwork. Furthermore, these extensions can also be utilized for the\npreprocessing of diffusion signals.","url_abs":"http://arxiv.org/abs/1808.01517v1","url_pdf":"http://arxiv.org/pdf/1808.01517v1.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":"delimit-pytorch-an-extension-for-deep","repo_url":"https://github.com/SimonKoppers/DELIMIT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}