{"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/towards-automated-melanoma-screening-proper","title":"Towards Automated Melanoma Screening: Proper Computer Vision & Reliable Results","arxiv_id":"1604.04024","date":"2016-04-14","proceeding":null,"authors":["Michel Fornaciali","Micael Carvalho","Flávia Vasques Bittencourt","Sandra Avila","Eduardo Valle"],"abstract":"In this paper we survey, analyze and criticize current art on automated\nmelanoma screening, reimplementing a baseline technique, and proposing two\nnovel ones. Melanoma, although highly curable when detected early, ends as one\nof the most dangerous types of cancer, due to delayed diagnosis and treatment.\nIts incidence is soaring, much faster than the number of trained professionals\nable to diagnose it. Automated screening appears as an alternative to make the\nmost of those professionals, focusing their time on the patients at risk while\nsafely discharging the other patients. However, the potential of automated\nmelanoma diagnosis is currently unfulfilled, due to the emphasis of current\nliterature on outdated computer vision models. Even more problematic is the\nirreproducibility of current art. We show how streamlined pipelines based upon\ncurrent Computer Vision outperform conventional models - a model based on an\nadvanced bags of words reaches an AUC of 84.6%, and a model based on deep\nneural networks reaches 89.3%, while the baseline (a classical bag of words)\nstays at 81.2%. We also initiate a dialog to improve reproducibility in our\ncommunity","url_abs":"http://arxiv.org/abs/1604.04024v3","url_pdf":"http://arxiv.org/pdf/1604.04024v3.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":"towards-automated-melanoma-screening-proper","repo_url":"https://github.com/learningtitans/data-depth-design","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"towards-automated-melanoma-screening-proper","repo_url":"https://github.com/learningtitans/isbi2017-part3","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"melanoma-diagnosis","task_name":"Melanoma Diagnosis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}