{"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","title":"Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)","arxiv_id":"1710.05006","date":"2017-10-13","proceeding":null,"authors":["Noel C. F. Codella","David Gutman","M. Emre Celebi","Brian Helba","Michael A. Marchetti","Stephen W. Dusza","Aadi Kalloo","Konstantinos Liopyris","Nabin Mishra","Harald Kittler","Allan Halpern"],"abstract":"This article describes the design, implementation, and results of the latest\ninstallment of the dermoscopic image analysis benchmark challenge. The goal is\nto support research and development of algorithms for automated diagnosis of\nmelanoma, the most lethal skin cancer. The challenge was divided into 3 tasks:\nlesion segmentation, feature detection, and disease classification.\nParticipation involved 593 registrations, 81 pre-submissions, 46 finalized\nsubmissions (including a 4-page manuscript), and approximately 50 attendees,\nmaking this the largest standardized and comparative study in this field to\ndate. 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