{"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/a-benchmark-for-endoluminal-scene","title":"A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images","arxiv_id":"1612.00799","date":"2016-12-02","proceeding":null,"authors":["David Vázquez","Jorge Bernal","F. Javier Sánchez","Gloria Fernández-Esparrach","Antonio M. López","Adriana Romero","Michal Drozdzal","Aaron Courville"],"abstract":"Colorectal cancer (CRC) is the third cause of cancer death worldwide.\nCurrently, the standard approach to reduce CRC-related mortality is to perform\nregular screening in search for polyps and colonoscopy is the screening tool of\nchoice. The main limitations of this screening procedure are polyp miss-rate\nand inability to perform visual assessment of polyp malignancy. These drawbacks\ncan be reduced by designing Decision Support Systems (DSS) aiming to help\nclinicians in the different stages of the procedure by providing endoluminal\nscene segmentation. Thus, in this paper, we introduce an extended benchmark of\ncolonoscopy image, with the hope of establishing a new strong benchmark for\ncolonoscopy image analysis research. We provide new baselines on this dataset\nby training standard fully convolutional networks (FCN) for semantic\nsegmentation and significantly outperforming, without any further\npost-processing, prior results in endoluminal scene segmentation.","url_abs":"http://arxiv.org/abs/1612.00799v1","url_pdf":"http://arxiv.org/pdf/1612.00799v1.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":"a-benchmark-for-endoluminal-scene","repo_url":"https://github.com/jbernoz/deeppolyp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"a-benchmark-for-endoluminal-scene","repo_url":"https://github.com/guilhermesantos/Semantic-Image-Segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"a-benchmark-for-endoluminal-scene","repo_url":"https://github.com/tfboys-lzz/fobs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"scene-segmentation","task_name":"Scene Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1612.00799","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.00799"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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