{"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/y-net-a-deep-convolutional-neural-network-for","title":"Y-Net: A deep Convolutional Neural Network for Polyp Detection","arxiv_id":"1806.01907","date":"2018-06-05","proceeding":null,"authors":["Ahmed Mohammed","Sule Yildirim","Ivar Farup","Marius Pedersen","Øistein Hovde"],"abstract":"Colorectal polyps are important precursors to colon cancer, the third most\ncommon cause of cancer mortality for both men and women. It is a disease where\nearly detection is of crucial importance. Colonoscopy is commonly used for\nearly detection of cancer and precancerous pathology. It is a demanding\nprocedure requiring significant amount of time from specialized physicians and\nnurses, in addition to a significant miss-rates of polyps by specialists.\nAutomated polyp detection in colonoscopy videos has been demonstrated to be a\npromising way to handle this problem. {However, polyps detection is a\nchallenging problem due to the availability of limited amount of training data\nand large appearance variations of polyps. To handle this problem, we propose a\nnovel deep learning method Y-Net that consists of two encoder networks with a\ndecoder network. Our proposed Y-Net method} relies on efficient use of\npre-trained and un-trained models with novel sum-skip-concatenation operations.\nEach of the encoders are trained with encoder specific learning rate along the\ndecoder. Compared with the previous methods employing hand-crafted features or\n2-D/3-D convolutional neural network, our approach outperforms state-of-the-art\nmethods for polyp detection with 7.3% F1-score and 13% recall improvement.","url_abs":"http://arxiv.org/abs/1806.01907v1","url_pdf":"http://arxiv.org/pdf/1806.01907v1.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":"y-net-a-deep-convolutional-neural-network-for","repo_url":"https://github.com/ahme0307/Ynet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"}],"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}