{"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/learning-an-mr-acquisition-invariant","title":"Learning an MR acquisition-invariant representation using Siamese neural networks","arxiv_id":"1810.07430","date":"2018-10-17","proceeding":null,"authors":["Wouter M. Kouw","Marco Loog","Wilbert Bartels","Adriënne M. Mendrik"],"abstract":"Generalization of voxelwise classifiers is hampered by differences between\nMRI-scanners, e.g. different acquisition protocols and field strengths. To\naddress this limitation, we propose a Siamese neural network (MRAI-NET) that\nextracts acquisition-invariant feature vectors. These can consequently be used\nby task-specific methods, such as voxelwise classifiers for tissue\nsegmentation. MRAI-NET is tested on both simulated and real patient data.\nExperiments show that MRAI-NET outperforms voxelwise classifiers trained on the\nsource or target scanner data when a small number of labeled samples is\navailable.","url_abs":"http://arxiv.org/abs/1810.07430v1","url_pdf":"http://arxiv.org/pdf/1810.07430v1.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":"learning-an-mr-acquisition-invariant","repo_url":"https://github.com/wmkouw/mrai-net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"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}