{"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-segmentation-using-atrous","title":"Skin Lesion Segmentation Using Atrous Convolution via DeepLab v3","arxiv_id":"1807.08891","date":"2018-07-24","proceeding":null,"authors":["Yujie Wang","Simon Sun","Jahow Yu","Dr. Limin Yu"],"abstract":"As melanoma diagnoses increase across the US, automated efforts to identify\nmalignant lesions become increasingly of interest to the research community.\nSegmentation of dermoscopic images is the first step in this process, thus\naccuracy is crucial. Although techniques utilizing convolutional neural\nnetworks have been used in the past for lesion segmentation, we present a\nsolution employing the recently published DeepLab 3, an atrous convolution\nmethod for image segmentation. Although the results produced by this run are\nnot ideal, with a mean Jaccard index of 0.498, we believe that with further\nadjustments and modifications to the compatibility with the DeepLab code and\nwith training on more powerful processing units, this method may achieve better\nresults in future trials.","url_abs":"http://arxiv.org/abs/1807.08891v1","url_pdf":"http://arxiv.org/pdf/1807.08891v1.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":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"lesion-segmentation","task_name":"Lesion Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"skin-lesion-segmentation","task_name":"Skin Lesion Segmentation"}],"methods":[{"method_slug":"crf","method_name":"CRF"},{"method_slug":"deeplab","method_name":"DeepLab"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dilated-convolution","method_name":"Dilated Convolution"},{"method_slug":"feedforward-network","method_name":"Feedforward Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/lesion-segmentation-on-university-of-waterloo","task":"Lesion Segmentation","dataset":"University of Waterloo skin cancer database","model":"DeepLabV3+","rank_in_archive_order":2,"of":5,"metrics":{"Dice score":"0.883 ±0.108 "},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}