{"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-synthesis-with-generative","title":"Skin Lesion Synthesis with Generative Adversarial Networks","arxiv_id":"1902.03253","date":"2019-02-08","proceeding":null,"authors":["Alceu Bissoto","Fábio Perez","Eduardo Valle","Sandra Avila"],"abstract":"Skin cancer is by far the most common type of cancer. Early detection is the\nkey to increase the chances for successful treatment significantly. Currently,\nDeep Neural Networks are the state-of-the-art results on automated skin cancer\nclassification. To push the results further, we need to address the lack of\nannotated data, which is expensive and require much effort from specialists. To\nbypass this problem, we propose using Generative Adversarial Networks for\ngenerating realistic synthetic skin lesion images. To the best of our\nknowledge, our results are the first to show visually-appealing synthetic\nimages that comprise clinically-meaningful information.","url_abs":"http://arxiv.org/abs/1902.03253v1","url_pdf":"http://arxiv.org/pdf/1902.03253v1.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":"skin-lesion-synthesis-with-generative","repo_url":"https://github.com/alceubissoto/gan-skin-lesion","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"skin-lesion-synthesis-with-generative","repo_url":"https://github.com/CristianLazoQuispe/skin-lesion-segmentation-using-pix2pix","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"cancer-classification","task_name":"Cancer Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"medical-image-generation","task_name":"Medical Image Generation"},{"task_slug":"skin-cancer-classification","task_name":"Skin Cancer Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1902.03253","atlas_url":"https://app.syntology.ai/?focus=1902.03253","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}