{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:22:24Z","timestamp":1778084544318,"version":"3.51.4"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164484","type":"print"},{"value":"9783031164491","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16449-1_47","type":"book-chapter","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T08:04:54Z","timestamp":1663315494000},"page":"497-506","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Retrieval of\u00a0Surgical Phase Transitions Using Reinforcement Learning"],"prefix":"10.1007","author":[{"given":"Yitong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sophia","family":"Bano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ann-Sophie","family":"Page","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Deprest","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danail","family":"Stoyanov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francisco","family":"Vasconcelos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,17]]},"reference":[{"issue":"9","key":"47_CR1","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1007\/s00192-009-0914-x","volume":"20","author":"F Claerhout","year":"2009","unstructured":"Claerhout, F., Roovers, J.P., Lewi, P., Verguts, J., De Ridder, D., Deprest, J.: Implementation of laparoscopic sacrocolpopexy-a single centre\u2019s experience. Int. Urogynecol. J. 20(9), 1119\u20131125 (2009)","journal-title":"Int. Urogynecol. J."},{"issue":"9","key":"47_CR2","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1007\/s00192-014-2412-z","volume":"25","author":"F Claerhout","year":"2014","unstructured":"Claerhout, F., Verguts, J., Werbrouck, E., Veldman, J., Lewi, P., Deprest, J.: Analysis of the learning process for laparoscopic sacrocolpopexy: identification of challenging steps. Int. Urogynecol. J. 25(9), 1185\u20131191 (2014). https:\/\/doi.org\/10.1007\/s00192-014-2412-z","journal-title":"Int. Urogynecol. J."},{"key":"47_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1007\/978-3-030-59716-0_33","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"T Czempiel","year":"2020","unstructured":"Czempiel, T., et al.: TeCNO: surgical phase recognition with multi-stage temporal convolutional networks. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 343\u2013352. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59716-0_33"},{"key":"47_CR4","doi-asserted-by":"crossref","unstructured":"Czempiel, T., Paschali, M., Ostler, D., Kim, S.T., Busam, B., Navab, N.: Opera: attention-regularized transformers for surgical phase recognition. arXiv preprint arXiv:2103.03873 (2021)","DOI":"10.1007\/978-3-030-87202-1_58"},{"key":"47_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1007\/978-3-319-46720-7_64","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"R DiPietro","year":"2016","unstructured":"DiPietro, R., et al.: Recognizing surgical activities with recurrent neural networks. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 551\u2013558. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46720-7_64"},{"key":"47_CR6","doi-asserted-by":"crossref","unstructured":"Gao, X., Jin, Y., Long, Y., Dou, Q., Heng, P.A.: Trans-SVNet: accurate phase recognition from surgical videos via hybrid embedding aggregation transformer. arXiv preprint arXiv:2103.09712 (2021)","DOI":"10.1007\/978-3-030-87202-1_57"},{"key":"47_CR7","unstructured":"Goodman, E.D., et al.: A real-time spatiotemporal AI model analyzes skill in open surgical videos. arXiv preprint arXiv:2112.07219 (2021)"},{"key":"47_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"47_CR9","doi-asserted-by":"crossref","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural comput. 9(8), 1735\u20131780 (12 1997)","DOI":"10.1162\/neco.1997.9.8.1735"},{"issue":"5","key":"47_CR10","doi-asserted-by":"publisher","first-page":"1114","DOI":"10.1109\/TMI.2017.2787657","volume":"37","author":"Y Jin","year":"2018","unstructured":"Jin, Y., et al.: SV-RCNet: workflow recognition from surgical videos using recurrent convolutional network. IEEE Trans. Med. Imaging 37(5), 1114\u20131126 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"47_CR11","doi-asserted-by":"crossref","unstructured":"Kawka, M., Gall, T.M., Fang, C., Liu, R., Jiao, L.R.: Intraoperative video analysis and machine learning models will change the future of surgical training. Intell. Surg. 1 (2021)","DOI":"10.1016\/j.isurg.2021.03.001"},{"key":"47_CR12","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"47_CR13","doi-asserted-by":"crossref","unstructured":"Lamblin, G., Chene, G., Warembourg, S., Jacquot, F., Moret, S., Golfier, F.: Glue mesh fixation in laparoscopic sacrocolpopexy: results at 3 years\u2019 follow-up. Int. Urogynecol. J. 33(9), 2533\u20132541 (2021)","DOI":"10.1007\/s00192-021-04764-4"},{"key":"47_CR14","doi-asserted-by":"publisher","unstructured":"Lu, Y., Li, Y., Velipasalar, S.: Efficient human activity classification from egocentric videos incorporating actor-critic reinforcement learning. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 564\u2013568 (2019). https:\/\/doi.org\/10.1109\/ICIP.2019.8803823","DOI":"10.1109\/ICIP.2019.8803823"},{"key":"47_CR15","doi-asserted-by":"crossref","unstructured":"Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529\u2013533 (2015)","DOI":"10.1038\/nature14236"},{"key":"47_CR16","doi-asserted-by":"publisher","unstructured":"Nikpour, B., Armanfard, N.: Joint selection using deep reinforcement learning for skeleton-based activity recognition. In: 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1056\u20131061 (2021). https:\/\/doi.org\/10.1109\/SMC52423.2021.9659047","DOI":"10.1109\/SMC52423.2021.9659047"},{"issue":"2","key":"47_CR17","doi-asserted-by":"publisher","first-page":"2365","DOI":"10.1109\/LRA.2021.3060410","volume":"6","author":"J Park","year":"2021","unstructured":"Park, J., Park, C.H.: Recognition and prediction of surgical actions based on online robotic tool detection. IEEE Robot. Autom. Lett. 6(2), 2365\u20132372 (2021). https:\/\/doi.org\/10.1109\/LRA.2021.3060410","journal-title":"IEEE Robot. Autom. Lett."},{"key":"47_CR18","unstructured":"Rojas-Mu\u00f1oz, E., Couperus, K., Wachs, J.: DAISI: database for AI surgical instruction. arXiv preprint arXiv:2004.02809 (2020)"},{"key":"47_CR19","unstructured":"Sarikaya, D., Jannin, P.: Towards generalizable surgical activity recognition using spatial temporal graph convolutional networks. arXiv preprint arXiv:2001.03728 (2020)"},{"issue":"1","key":"47_CR20","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1109\/TMI.2016.2593957","volume":"36","author":"AP Twinanda","year":"2016","unstructured":"Twinanda, A.P., Shehata, S., Mutter, D., Marescaux, J., De Mathelin, M., Padoy, N.: EndoNet: a deep architecture for recognition tasks on laparoscopic videos. IEEE Trans. Med. Imaging 36(1), 86\u201397 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"47_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1889681.1889687","volume":"2","author":"JA Ward","year":"2011","unstructured":"Ward, J.A., Lukowicz, P., Gellersen, H.W.: Performance metrics for activity recognition. ACM Trans. Intell. Syst. Technol. (TIST) 2(1), 1\u201323 (2011)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16449-1_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:58:16Z","timestamp":1709830696000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16449-1_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164484","9783031164491"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16449-1_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"17 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"574","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}