{"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-2","title":"Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)","arxiv_id":"1605.01397","date":"2016-05-04","proceeding":null,"authors":["David Gutman","Noel C. F. Codella","Emre Celebi","Brian Helba","Michael Marchetti","Nabin Mishra","Allan Halpern"],"abstract":"In this article, we describe the design and implementation of a publicly\naccessible dermatology image analysis benchmark challenge. The goal of the\nchallenge is to sup- port research and development of algorithms for automated\ndiagnosis of melanoma, a lethal form of skin cancer, from dermoscopic images.\nThe challenge was divided into sub-challenges for each task involved in image\nanalysis, including lesion segmentation, dermoscopic feature detection within a\nlesion, and classification of melanoma. Training data included 900 images. A\nseparate test dataset of 379 images was provided to measure resultant\nperformance of systems developed with the training data. Ground truth for both\ntraining and test sets was generated by a panel of dermoscopic experts. In\ntotal, there were 79 submissions from a group of 38 participants, making this\nthe largest standardized and comparative study for melanoma diagnosis in\ndermoscopic images to date. While the official challenge duration and ranking\nof participants has concluded, the datasets remain available for further\nresearch and development.","url_abs":"http://arxiv.org/abs/1605.01397v1","url_pdf":"http://arxiv.org/pdf/1605.01397v1.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":[],"tasks":[{"task_slug":"lesion-segmentation","task_name":"Lesion Segmentation"},{"task_slug":"melanoma-diagnosis","task_name":"Melanoma Diagnosis"}],"methods":[],"datasets_introduced":[{"slug":"isic2016-task-1","name":"ISIC2016","full_name":"Lesion Segmentation"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1605.01397","atlas_url":"https://app.syntology.ai/?focus=1605.01397","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}