{"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/adversarial-deep-structured-nets-for-mass","title":"Adversarial Deep Structured Nets for Mass Segmentation from Mammograms","arxiv_id":"1710.09288","date":"2017-10-24","proceeding":null,"authors":["Wentao Zhu","Xiang Xiang","Trac. D. Tran","Gregory D. Hager","Xiaohui Xie"],"abstract":"Mass segmentation provides effective morphological features which are\nimportant for mass diagnosis. In this work, we propose a novel end-to-end\nnetwork for mammographic mass segmentation which employs a fully convolutional\nnetwork (FCN) to model a potential function, followed by a CRF to perform\nstructured learning. Because the mass distribution varies greatly with pixel\nposition, the FCN is combined with a position priori. Further, we employ\nadversarial training to eliminate over-fitting due to the small sizes of\nmammogram datasets. Multi-scale FCN is employed to improve the segmentation\nperformance. Experimental results on two public datasets, INbreast and\nDDSM-BCRP, demonstrate that our end-to-end network achieves better performance\nthan state-of-the-art approaches.\n\\footnote{https://github.com/wentaozhu/adversarial-deep-structural-networks.git}","url_abs":"http://arxiv.org/abs/1710.09288v2","url_pdf":"http://arxiv.org/pdf/1710.09288v2.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":"adversarial-deep-structured-nets-for-mass","repo_url":"https://github.com/wentaozhu/adversarial-deep-structural-networks","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"mass-segmentation-from-mammograms","task_name":"Mass Segmentation From Mammograms"},{"task_slug":null,"task_name":"Position"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[{"method_slug":"crf","method_name":"CRF"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"fcn","method_name":"FCN"},{"method_slug":"max-pooling","method_name":"Max Pooling"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}