{"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/temporal-coherence-based-self-supervised","title":"Temporal coherence-based self-supervised learning for laparoscopic workflow analysis","arxiv_id":"1806.06811","date":"2018-06-18","proceeding":null,"authors":["Isabel Funke","Alexander Jenke","Sören Torge Mees","Jürgen Weitz","Stefanie Speidel","Sebastian Bodenstedt"],"abstract":"In order to provide the right type of assistance at the right time,\ncomputer-assisted surgery systems need context awareness. To achieve this,\nmethods for surgical workflow analysis are crucial. Currently, convolutional\nneural networks provide the best performance for video-based workflow analysis\ntasks. For training such networks, large amounts of annotated data are\nnecessary. However, collecting a sufficient amount of data is often costly,\ntime-consuming, and not always feasible. In this paper, we address this problem\nby presenting and comparing different approaches for self-supervised\npretraining of neural networks on unlabeled laparoscopic videos using temporal\ncoherence. We evaluate our pretrained networks on Cholec80, a publicly\navailable dataset for surgical phase segmentation, on which a maximum F1 score\nof 84.6 was reached. Furthermore, we were able to achieve an increase of the F1\nscore of up to 10 points when compared to a non-pretrained neural network.","url_abs":"http://arxiv.org/abs/1806.06811v2","url_pdf":"http://arxiv.org/pdf/1806.06811v2.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":"temporal-coherence-based-self-supervised","repo_url":"https://gitlab.com/nct_tso_public/pretrain_tc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"},{"task_slug":"surgical-phase-recognition","task_name":"Surgical phase recognition"}],"methods":[{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}