{"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/domain-adaptation-for-mri-organ-segmentation","title":"Domain Adaptation for MRI Organ Segmentation using Reverse Classification Accuracy","arxiv_id":"1806.00363","date":"2018-06-01","proceeding":null,"authors":["Vanya V. Valindria","Ioannis Lavdas","Wenjia Bai","Konstantinos Kamnitsas","Eric O. Aboagye","Andrea G. Rockall","Daniel Rueckert","Ben Glocker"],"abstract":"The variations in multi-center data in medical imaging studies have brought\nthe necessity of domain adaptation. Despite the advancement of machine learning\nin automatic segmentation, performance often degrades when algorithms are\napplied on new data acquired from different scanners or sequences than the\ntraining data. Manual annotation is costly and time consuming if it has to be\ncarried out for every new target domain. In this work, we investigate automatic\nselection of suitable subjects to be annotated for supervised domain adaptation\nusing the concept of reverse classification accuracy (RCA). RCA predicts the\nperformance of a trained model on data from the new domain and different\nstrategies of selecting subjects to be included in the adaptation via transfer\nlearning are evaluated. We perform experiments on a two-center MR database for\nthe task of organ segmentation. We show that subject selection via RCA can\nreduce the burden of annotation of new data for the target domain.","url_abs":"http://arxiv.org/abs/1806.00363v1","url_pdf":"http://arxiv.org/pdf/1806.00363v1.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":"domain-adaptation-for-mri-organ-segmentation","repo_url":"https://github.com/Kamnitsask/deepmedic","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"organ-segmentation","task_name":"Organ Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"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}