{"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/automatic-skin-lesion-segmentation-using-deep","title":"Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks","arxiv_id":"1807.06466","date":"2018-07-17","proceeding":null,"authors":["Hongming Xu","Tae Hyun Hwang"],"abstract":"This paper summarizes our method and validation results for the ISIC\nChallenge 2018 - Skin Lesion Analysis Towards Melanoma Detection - Task 1:\nLesion Segmentation","url_abs":"http://arxiv.org/abs/1807.06466v1","url_pdf":"http://arxiv.org/pdf/1807.06466v1.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":"automatic-skin-lesion-segmentation-using-deep","repo_url":"https://github.com/RegulusReggie/CS259","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"lesion-segmentation","task_name":"Lesion Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"skin-lesion-segmentation","task_name":"Skin Lesion Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}