{"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/skin-lesion-analysis-toward-melanoma-1","title":"Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)","arxiv_id":"1902.03368","date":"2019-02-09","proceeding":null,"authors":["Noel Codella","Veronica Rotemberg","Philipp Tschandl","M. Emre Celebi","Stephen Dusza","David Gutman","Brian Helba","Aadi Kalloo","Konstantinos Liopyris","Michael Marchetti","Harald Kittler","Allan Halpern"],"abstract":"This work summarizes the results of the largest skin image analysis challenge\nin the world, hosted by the International Skin Imaging Collaboration (ISIC), a\nglobal partnership that has organized the world's largest public repository of\ndermoscopic images of skin. The challenge was hosted in 2018 at the Medical\nImage Computing and Computer Assisted Intervention (MICCAI) conference in\nGranada, Spain. The dataset included over 12,500 images across 3 tasks. 900\nusers registered for data download, 115 submitted to the lesion segmentation\ntask, 25 submitted to the lesion attribute detection task, and 159 submitted to\nthe disease classification task. Novel evaluation protocols were established,\nincluding a new test for segmentation algorithm performance, and a test for\nalgorithm ability to generalize. Results show that top segmentation algorithms\nstill fail on over 10% of images on average, and algorithms with equal\nperformance on test data can have different abilities to generalize. This is an\nimportant consideration for agencies regulating the growing set of machine\nlearning tools in the healthcare domain, and sets a new standard for future\npublic challenges in healthcare.","url_abs":"http://arxiv.org/abs/1902.03368v2","url_pdf":"http://arxiv.org/pdf/1902.03368v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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