{"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/darcnn-domain-adaptive-region-based-1","title":"DARCNN: Domain Adaptive Region-based Convolutional Neural Network forUnsupervised Instance Segmentation in Biomedical Images","arxiv_id":null,"date":"2021-04-03","proceeding":"CVPR 2021 2021 4","authors":["Joy Hsu"],"abstract":"In  the  biomedical  domain,  there  is  an  abundance  ofdense, complex data where objects of interest may be chal-lenging to detect or constrained by limits of human knowl-edge.    Labelled  domain  specific  datasets  for  supervisedtasks  are  often  expensive  to  obtain,  and  furthermore  dis-covery  of  novel  distinct  objects  may  be  desirable  for  un-biased scientific discovery.  Therefore, we propose leverag-ing the wealth of annotations in benchmark computer visiondatasets to conduct unsupervised instance segmentation fordiverse biomedical datasets.  The key obstacle is thus over-coming the large domain shift from common to biomedicalimages. We propose a Domain Adaptive Region-based Con-volutional Neural Network (DARCNN), that adapts knowl-edge of object definition from COCO, a large labelled visiondataset, to multiple biomedical datasets. We introduce a do-main  separation  module,  a  self-supervised  representationconsistency loss, and an augmented pseudo-labelling stagewithin DARCNN to effectively perform domain adaptationacross such large domain shifts.  We showcase DARCNN’sperformance for unsupervised instance segmentation on nu-merous biomedical datasets.","url_abs":"https://arxiv.org/abs/2104.01325","url_pdf":"https://arxiv.org/abs/2104.01325","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":"darcnn-domain-adaptive-region-based-1","repo_url":"https://github.com/joyhsu0504/darcnn","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"unsupervised-instance-segmentation","task_name":"Unsupervised Instance Segmentation"},{"task_slug":"scientific-discovery","task_name":"scientific discovery"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}