{"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/unsupervised-domain-adaptation-in-brain","title":"Unsupervised domain adaptation in brain lesion segmentation with adversarial networks","arxiv_id":"1612.08894","date":"2016-12-28","proceeding":null,"authors":["Konstantinos Kamnitsas","Christian Baumgartner","Christian Ledig","Virginia F. J. Newcombe","Joanna P. Simpson","Andrew D. Kane","David K. Menon","Aditya Nori","Antonio Criminisi","Daniel Rueckert","Ben Glocker"],"abstract":"Significant advances have been made towards building accurate automatic\nsegmentation systems for a variety of biomedical applications using machine\nlearning. However, the performance of these systems often degrades when they\nare applied on new data that differ from the training data, for example, due to\nvariations in imaging protocols. Manually annotating new data for each test\ndomain is not a feasible solution. In this work we investigate unsupervised\ndomain adaptation using adversarial neural networks to train a segmentation\nmethod which is more invariant to differences in the input data, and which does\nnot require any annotations on the test domain. Specifically, we learn\ndomain-invariant features by learning to counter an adversarial network, which\nattempts to classify the domain of the input data by observing the activations\nof the segmentation network. Furthermore, we propose a multi-connected domain\ndiscriminator for improved adversarial training. Our system is evaluated using\ntwo MR databases of subjects with traumatic brain injuries, acquired using\ndifferent scanners and imaging protocols. Using our unsupervised approach, we\nobtain segmentation accuracies which are close to the upper bound of supervised\ndomain adaptation.","url_abs":"http://arxiv.org/abs/1612.08894v1","url_pdf":"http://arxiv.org/pdf/1612.08894v1.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":"unsupervised-domain-adaptation-in-brain","repo_url":"https://github.com/wenhui0206/MeanTeacher-DeepMedic","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"lesion-segmentation","task_name":"Lesion Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1612.08894","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}