{"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/weakly-supervised-learning-for-tool","title":"Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos","arxiv_id":"1806.05573","date":"2018-06-14","proceeding":null,"authors":["Armine Vardazaryan","Didier Mutter","Jacques Marescaux","Nicolas Padoy"],"abstract":"Surgical tool localization is an essential task for the automatic analysis of\nendoscopic videos. In the literature, existing methods for tool localization,\ntracking and segmentation require training data that is fully annotated,\nthereby limiting the size of the datasets that can be used and the\ngeneralization of the approaches. In this work, we propose to circumvent the\nlack of annotated data with weak supervision. We propose a deep architecture,\ntrained solely on image level annotations, that can be used for both tool\npresence detection and localization in surgical videos. Our architecture relies\non a fully convolutional neural network, trained end-to-end, enabling us to\nlocalize surgical tools without explicit spatial annotations. We demonstrate\nthe benefits of our approach on a large public dataset, Cholec80, which is\nfully annotated with binary tool presence information and of which 5 videos\nhave been fully annotated with bounding boxes and tool centers for the\nevaluation.","url_abs":"http://arxiv.org/abs/1806.05573v2","url_pdf":"http://arxiv.org/pdf/1806.05573v2.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":"weakly-supervised-learning-for-tool","repo_url":"https://github.com/CAMMA-public/ai4surgery","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"surgical-tool-detection","task_name":"Surgical tool detection"},{"task_slug":"weakly-supervised-learning","task_name":"Weakly-supervised Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/surgical-tool-detection-on-cholec80","task":"Surgical tool detection","dataset":"Cholec80","model":"FCN","rank_in_archive_order":4,"of":6,"metrics":{"mAP":"87.4"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}