{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:53:41Z","timestamp":1743018821217,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031164484"},{"type":"electronic","value":"9783031164491"}],"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_40","type":"book-chapter","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T08:04:54Z","timestamp":1663315494000},"page":"421-430","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Nonlinear Regression of\u00a0Remaining Surgical Duration via\u00a0Bayesian LSTM-Based Deep Negative Correlation Learning"],"prefix":"10.1007","author":[{"given":"Junyang","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rong","family":"Tao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoyan","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,17]]},"reference":[{"key":"40_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1007\/978-3-319-66185-8_66","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"I Aksamentov","year":"2017","unstructured":"Aksamentov, I., Twinanda, A.P., Mutter, D., Marescaux, J., Padoy, N.: Deep Neural Networks Predict Remaining Surgery Duration from Cholecystectomy Videos. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 586\u2013593. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66185-8_66"},{"key":"40_CR2","unstructured":"Bhatia, B., Oates, T., Xiao, Y., Hu, P.: Real-time identification of operating room state from video. In: AAAI, vol. 2, pp. 1761\u20131766 (2007)"},{"key":"40_CR3","doi-asserted-by":"crossref","unstructured":"Bricon-Souf, N., Newman, C.R.: Context awareness in health care: a review. Int. J. Med. Inform. 76(1), 2\u201312 (2007)","DOI":"10.1016\/j.ijmedinf.2006.01.003"},{"issue":"2","key":"40_CR4","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/s10916-010-9486-z","volume":"36","author":"SP Devi","year":"2012","unstructured":"Devi, S.P., Rao, K.S., Sangeetha, S.S.: Prediction of surgery times and scheduling of operation theaters in optholmology department. J. Med. Syst. 36(2), 415\u2013430 (2012)","journal-title":"J. Med. Syst."},{"key":"40_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/3-540-45014-9_1","volume-title":"Multiple Classifier Systems","author":"TG Dietterich","year":"2000","unstructured":"Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1\u201315. Springer, Heidelberg (2000). https:\/\/doi.org\/10.1007\/3-540-45014-9_1"},{"key":"40_CR6","doi-asserted-by":"publisher","first-page":"85","DOI":"10.3389\/fmed.2017.00085","volume":"4","author":"ER Edelman","year":"2017","unstructured":"Edelman, E.R., Van Kuijk, S.M., Hamaekers, A.E., De Korte, M.J., Van Merode, G.G., Buhre, W.F.: Improving the prediction of total surgical procedure time using linear regression modeling. Front. Med. 4, 85 (2017)","journal-title":"Front. Med."},{"issue":"1","key":"40_CR7","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1097\/ALN.0b013e3181c294c2","volume":"112","author":"MJ Eijkemans","year":"2010","unstructured":"Eijkemans, M.J., Van Houdenhoven, M., Nguyen, T., Boersma, E., Steyerberg, E.W., Kazemier, G.: Predicting the unpredictable: a new prediction model for operating room times using individual characteristics and the surgeon\u2019s estimate. J. Am. Soc. Anesthesiol. 112(1), 41\u201349 (2010)","journal-title":"J. Am. Soc. Anesthesiol."},{"key":"40_CR8","unstructured":"Gal, Y., Ghahramani, Z.: Bayesian convolutional neural networks with Bernoulli approximate variational inference. arXiv preprint arXiv:1506.02158 (2016)"},{"key":"40_CR9","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: International Conference on Machine Learning, pp. 1050\u20131059. PMLR (2016)"},{"key":"40_CR10","unstructured":"Gal, Y., Ghahramani, Z.: A theoretically grounded application of dropout in recurrent neural networks. Adv. Neural Inf. Process. Syst. 29 (2016)"},{"key":"40_CR11","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"40_CR12","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? Adv. Neural Inf. Process. Syst. 30 (2017)"},{"issue":"1","key":"40_CR13","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/JBHI.2016.2635663","volume":"21","author":"A Kumar","year":"2016","unstructured":"Kumar, A., Kim, J., Lyndon, D., Fulham, M., Feng, D.: An ensemble of fine-tuned convolutional neural networks for medical image classification. IEEE J. Biomed. Health Inform. 21(1), 31\u201340 (2016)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"4","key":"40_CR14","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1109\/4235.887237","volume":"4","author":"Y Liu","year":"2000","unstructured":"Liu, Y., Yao, X., Higuchi, T.: Evolutionary ensembles with negative correlation learning. IEEE Trans. Evol. Comput. 4(4), 380\u2013387 (2000)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"40_CR15","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)"},{"key":"40_CR16","doi-asserted-by":"publisher","unstructured":"Marafioti, A., et al.: CataNet: predicting remaining cataract surgery duration. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 426\u2013435. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87202-1_41","DOI":"10.1007\/978-3-030-87202-1_41"},{"key":"40_CR17","doi-asserted-by":"publisher","unstructured":"Rivoir, D., et al.: Rethinking anticipation tasks: uncertainty-aware anticipation of sparse surgical instrument usage for context-aware assistance. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 752\u2013762. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59716-0_72","DOI":"10.1007\/978-3-030-59716-0_72"},{"key":"40_CR18","doi-asserted-by":"crossref","unstructured":"Schoeffmann, K., Taschwer, M., Sarny, S., M\u00fcnzer, B., Primus, M.J., Putzgruber, D.: Cataract-101: video dataset of 101 cataract surgeries. In: Proceedings of the 9th ACM Multimedia Systems Conference, pp. 421\u2013425 (2018)","DOI":"10.1145\/3204949.3208137"},{"key":"40_CR19","doi-asserted-by":"crossref","unstructured":"Sert, E., Ertekin, S., Halici, U.: Ensemble of convolutional neural networks for classification of breast microcalcification from mammograms. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 689\u2013692. IEEE (2017)","DOI":"10.1109\/EMBC.2017.8036918"},{"issue":"4","key":"40_CR20","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1109\/TMI.2018.2878055","volume":"38","author":"AP Twinanda","year":"2019","unstructured":"Twinanda, A.P., Yengera, G., Mutter, D., Marescaux, J., Padoy, N.: RSDNet: learning to predict remaining surgery duration from laparoscopic videos without manual annotations. IEEE Trans. Med. Imaging 38(4), 1069\u20131078 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"40_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2021.100633","volume":"25","author":"DR Yuniartha","year":"2021","unstructured":"Yuniartha, D.R., Masruroh, N.A., Herliansyah, M.K.: An evaluation of a simple model for predicting surgery duration using a set of surgical procedure parameters. Inform. Med. Unlocked 25, 100633 (2021)","journal-title":"Inform. Med. Unlocked"},{"key":"40_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, L., et al.: Nonlinear regression via deep negative correlation learning. IEEE Trans. Pattern Anal. Mach. Intell. 43(3), 982\u2013998 (2021)","DOI":"10.1109\/TPAMI.2019.2943860"}],"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_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:57:06Z","timestamp":1709830626000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16449-1_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164484","9783031164491"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16449-1_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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)"}}]}}