{"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/endonet-a-deep-architecture-for-recognition","title":"EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos","arxiv_id":"1602.03012","date":"2016-02-09","proceeding":null,"authors":["Andru P. Twinanda","Sherif Shehata","Didier Mutter","Jacques Marescaux","Michel de Mathelin","Nicolas Padoy"],"abstract":"Surgical workflow recognition has numerous potential medical applications,\nsuch as the automatic indexing of surgical video databases and the optimization\nof real-time operating room scheduling, among others. As a result, phase\nrecognition has been studied in the context of several kinds of surgeries, such\nas cataract, neurological, and laparoscopic surgeries. In the literature, two\ntypes of features are typically used to perform this task: visual features and\ntool usage signals. However, the visual features used are mostly handcrafted.\nFurthermore, the tool usage signals are usually collected via a manual\nannotation process or by using additional equipment. In this paper, we propose\na novel method for phase recognition that uses a convolutional neural network\n(CNN) to automatically learn features from cholecystectomy videos and that\nrelies uniquely on visual information. In previous studies, it has been shown\nthat the tool signals can provide valuable information in performing the phase\nrecognition task. Thus, we present a novel CNN architecture, called EndoNet,\nthat is designed to carry out the phase recognition and tool presence detection\ntasks in a multi-task manner. To the best of our knowledge, this is the first\nwork proposing to use a CNN for multiple recognition tasks on laparoscopic\nvideos. Extensive experimental comparisons to other methods show that EndoNet\nyields state-of-the-art results for both tasks.","url_abs":"http://arxiv.org/abs/1602.03012v2","url_pdf":"http://arxiv.org/pdf/1602.03012v2.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":"endonet-a-deep-architecture-for-recognition","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"}},{"paper_slug":"endonet-a-deep-architecture-for-recognition","repo_url":"https://github.com/CAMMA-public/cholect50","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"endonet-a-deep-architecture-for-recognition","repo_url":"https://github.com/YuemingJin/MTRCNet-CL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"endonet-a-deep-architecture-for-recognition","repo_url":"https://github.com/YuemingJin/TMRNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"endonet-a-deep-architecture-for-recognition","repo_url":"https://github.com/camma-public/ssg-qa","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"endonet-a-deep-architecture-for-recognition","repo_url":"https://github.com/camma-public/ssg-vqa","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"endonet-a-deep-architecture-for-recognition","repo_url":"https://github.com/camma-public/tf-cholec80","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"endonet-a-deep-architecture-for-recognition","repo_url":"https://gitlab.com/nct_tso_public/ins_ant","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"endonet-a-deep-architecture-for-recognition","repo_url":"https://gitlab.com/nct_tso_public/pitfalls_bn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"offline-surgical-phase-recognition","task_name":"Offline surgical phase recognition"},{"task_slug":"online-surgical-phase-recognition","task_name":"Online surgical phase recognition"},{"task_slug":"scheduling","task_name":"Scheduling"},{"task_slug":"surgical-tool-detection","task_name":"Surgical tool detection"}],"methods":[],"datasets_introduced":[{"slug":"cholec80","name":"Cholec80","full_name":"Surgical Workflow Dataset"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/surgical-tool-detection-on-cholec80","task":"Surgical tool detection","dataset":"Cholec80","model":"EndoNet","rank_in_archive_order":5,"of":6,"metrics":{"mAP":"81.0"},"uses_additional_data":false},{"leaderboard":"/sota/surgical-tool-detection-on-cholec80","task":"Surgical tool detection","dataset":"Cholec80","model":"ToolNet","rank_in_archive_order":6,"of":6,"metrics":{"mAP":"80.9"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1602.03012","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1602.03012"}},"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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