{"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/incorporating-the-knowledge-of-dermatologists","title":"Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for the Diagnosis of Skin Lesions","arxiv_id":"1703.01976","date":"2017-03-06","proceeding":null,"authors":["Iván González Díaz"],"abstract":"This report describes our submission to the ISIC 2017 Challenge in Skin\nLesion Analysis Towards Melanoma Detection. We have participated in the Part 3:\nLesion Classification with a system for automatic diagnosis of nevus, melanoma\nand seborrheic keratosis. Our approach aims to incorporate the expert knowledge\nof dermatologists into the well known framework of Convolutional Neural\nNetworks (CNN), which have shown impressive performance in many visual\nrecognition tasks. In particular, we have designed several networks providing\nlesion area identification, lesion segmentation into structural patterns and\nfinal diagnosis of clinical cases. Furthermore, novel blocks for CNNs have been\ndesigned to integrate this information with the diagnosis processing pipeline.","url_abs":"http://arxiv.org/abs/1703.01976v3","url_pdf":"http://arxiv.org/pdf/1703.01976v3.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":"incorporating-the-knowledge-of-dermatologists","repo_url":"https://github.com/igondia/matconvnet-dermoscopy","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"incorporating-the-knowledge-of-dermatologists","repo_url":"https://github.com/Abdulrahman-Adel/Skin-Cancer-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"lesion-classification","task_name":"Lesion Classification"},{"task_slug":"lesion-segmentation","task_name":"Lesion Segmentation"}],"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}