{"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/cost-effective-active-learning-for-melanoma","title":"Cost-Effective Active Learning for Melanoma Segmentation","arxiv_id":"1711.09168","date":"2017-11-24","proceeding":null,"authors":["Marc Gorriz","Axel Carlier","Emmanuel Faure","Xavier Giro-i-Nieto"],"abstract":"We propose a novel Active Learning framework capable to train effectively a\nconvolutional neural network for semantic segmentation of medical imaging, with\na limited amount of training labeled data. Our contribution is a practical\nCost-Effective Active Learning approach using dropout at test time as Monte\nCarlo sampling to model the pixel-wise uncertainty and to analyze the image\ninformation to improve the training performance. The source code of this\nproject is available at\nhttps://marc-gorriz.github.io/CEAL-Medical-Image-Segmentation/ .","url_abs":"http://arxiv.org/abs/1711.09168v2","url_pdf":"http://arxiv.org/pdf/1711.09168v2.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":"cost-effective-active-learning-for-melanoma","repo_url":"https://github.com/imatge-upc/medical-2017-nipsw","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"cost-effective-active-learning-for-melanoma","repo_url":"https://github.com/marc-gorriz/CEAL-Medical-Image-Segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"dropout","method_name":"Dropout"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.09168","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}