{"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-segmentation-of-skin-lesions-using","title":"Automatic segmentation of skin lesions using deep learning","arxiv_id":"1807.04893","date":"2018-07-13","proceeding":null,"authors":["Joshua Peter Ebenezer","Jagath C. Rajapakse"],"abstract":"This paper summarizes the method used in our submission to Task 1 of the\nInternational Skin Imaging Collaboration's (ISIC) Skin Lesion Analysis Towards\nMelanoma Detection challenge held in 2018. We used a fully automated method to\naccurately segment lesion boundaries from dermoscopic images. A U-net deep\nlearning network is trained on publicly available data from ISIC. We introduce\nthe use of intensity, color, and texture enhancement operations as\npre-processing steps and morphological operations and contour identification as\npost-processing steps.","url_abs":"http://arxiv.org/abs/1807.04893v1","url_pdf":"http://arxiv.org/pdf/1807.04893v1.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-segmentation-of-skin-lesions-using","repo_url":"https://github.com/JoshuaEbenezer/deep_segment","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}