{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:45:53Z","timestamp":1765547153789,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031439957"},{"type":"electronic","value":"9783031439964"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-43996-4_47","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:07:48Z","timestamp":1696115268000},"page":"494-504","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Imitation Learning from\u00a0Expert Video Data for\u00a0Dissection Trajectory Prediction in\u00a0Endoscopic Surgical Procedure"],"prefix":"10.1007","author":[{"given":"Jianan","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yueming","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yueyao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hon-Chi","family":"Yip","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Markus","family":"Scheppach","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philip Wai-Yan","family":"Chiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yeung","family":"Yam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Helen Mei-Ling","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Dou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"47_CR1","unstructured":"Allan, M., et al.: 2018 robotic scene segmentation challenge. arXiv preprint arXiv:2001.11190 (2020)"},{"key":"47_CR2","doi-asserted-by":"publisher","first-page":"3584","DOI":"10.1007\/s00464-012-2371-8","volume":"26","author":"PWY Chiu","year":"2012","unstructured":"Chiu, P.W.Y., et al.: Endoscopic submucosal dissection (ESD) compared with gastrectomy for treatment of early gastric neoplasia: a retrospective cohort study. Surg. Endosc. 26, 3584\u20133591 (2012)","journal-title":"Surg. Endosc."},{"key":"47_CR3","doi-asserted-by":"crossref","unstructured":"Codevilla, F., Santana, E., L\u00f3pez, A.M., Gaidon, A.: Exploring the limitations of behavior cloning for autonomous driving. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9329\u20139338 (2019)","DOI":"10.1109\/ICCV.2019.00942"},{"key":"47_CR4","unstructured":"Du, Y., Mordatch, I.: Implicit generation and modeling with energy based models. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"47_CR5","unstructured":"Florence, P., et al.: Implicit behavioral cloning. In: Conference on Robot Learning, pp. 158\u2013168. PMLR (2022)"},{"key":"47_CR6","doi-asserted-by":"crossref","unstructured":"Ganapathi, A., Florence, P., Varley, J., Burns, K., Goldberg, K., Zeng, A.: Implicit kinematic policies: unifying joint and cartesian action spaces in end-to-end robot learning. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 2656\u20132662. IEEE (2022)","DOI":"10.1109\/ICRA46639.2022.9812165"},{"issue":"4","key":"47_CR7","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1097\/SLA.0000000000004425","volume":"273","author":"CR Garrow","year":"2021","unstructured":"Garrow, C.R., et al.: Machine learning for surgical phase recognition: a systematic review. Ann. Surg. 273(4), 684\u2013693 (2021)","journal-title":"Ann. Surg."},{"key":"47_CR8","doi-asserted-by":"crossref","unstructured":"Gu, T., et al.: Stochastic trajectory prediction via motion indeterminacy diffusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 17113\u201317122 (2022)","DOI":"10.1109\/CVPR52688.2022.01660"},{"key":"47_CR9","doi-asserted-by":"crossref","unstructured":"Guo, J., Sun, Y., Guo, S.: A novel trajectory predicting method of catheter for the vascular interventional surgical robot. In: IEEE International Conference on Mechatronics and Automation, pp. 1304\u20131309 (2020)","DOI":"10.1109\/ICMA49215.2020.9233663"},{"key":"47_CR10","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol. 33, pp. 6840\u20136851 (2020)"},{"issue":"2","key":"47_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3054912","volume":"50","author":"A Hussein","year":"2017","unstructured":"Hussein, A., Gaber, M.M., Elyan, E., Jayne, C.: Imitation learning: a survey of learning methods. ACM Comput. Surv. 50(2), 1\u201335 (2017)","journal-title":"ACM Comput. Surv."},{"key":"47_CR12","unstructured":"Jarrett, D., Bica, I., van der Schaar, M.: Strictly batch imitation learning by energy-based distribution matching. In: Advances in Neural Information Processing Systems, vol. 33, pp. 7354\u20137365 (2020)"},{"issue":"12","key":"47_CR13","doi-asserted-by":"publisher","first-page":"2193","DOI":"10.1007\/s11548-022-02743-8","volume":"17","author":"Y Jin","year":"2022","unstructured":"Jin, Y., Long, Y., Gao, X., Stoyanov, D., Dou, Q., Heng, P.A.: Trans-svnet: hybrid embedding aggregation transformer for surgical workflow analysis. Int. J. Comput. Assist. Radiol. Surg. 17(12), 2193\u20132202 (2022)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"47_CR14","doi-asserted-by":"crossref","unstructured":"Ke, L., Choudhury, S., Barnes, M., Sun, W., Lee, G., Srinivasa, S.: Imitation learning as f-divergence minimization. In: Algorithmic Foundations of Robotics XIV: Proceedings of the Fourteenth Workshop on the Algorithmic Foundations of Robotics, vol. 14. pp. 313\u2013329 (2021)","DOI":"10.1007\/978-3-030-66723-8_19"},{"issue":"07","key":"47_CR15","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1055\/s-0030-1256339","volume":"43","author":"E Kim","year":"2011","unstructured":"Kim, E., et al.: Factors predictive of perforation during endoscopic submucosal dissection for the treatment of colorectal tumors. Endoscopy 43(07), 573\u2013578 (2011)","journal-title":"Endoscopy"},{"key":"47_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102079","volume":"71","author":"K Kl\u00e4ser","year":"2021","unstructured":"Kl\u00e4ser, K., et al.: Imitation learning for improved 3D pet\/MR attenuation correction. Med. Image Anal. 71, 102079 (2021)","journal-title":"Med. Image Anal."},{"issue":"3","key":"47_CR17","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1111\/j.1477-2574.2011.00425.x","volume":"14","author":"JM Laurence","year":"2012","unstructured":"Laurence, J.M., Tran, P.D., Richardson, A.J., Pleass, H.C., Lam, V.W.: Laparoscopic or open cholecystectomy in cirrhosis: a systematic review of outcomes and meta-analysis of randomized trials. HPB 14(3), 153\u2013161 (2012)","journal-title":"HPB"},{"issue":"9","key":"47_CR18","doi-asserted-by":"publisher","first-page":"14128","DOI":"10.1109\/TITS.2022.3144867","volume":"23","author":"L Le Mero","year":"2022","unstructured":"Le Mero, L., Yi, D., Dianati, M., Mouzakitis, A.: A survey on imitation learning techniques for end-to-end autonomous vehicles. IEEE Trans. Intell. Transp. Syst. 23(9), 14128\u201314147 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"47_CR19","unstructured":"Li, Y., Song, J., Ermon, S.: Infogail: interpretable imitation learning from visual demonstrations. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"issue":"2","key":"47_CR20","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1001\/jamasurg.2019.4917","volume":"155","author":"TJ Loftus","year":"2020","unstructured":"Loftus, T.J., et al.: Artificial intelligence and surgical decision-making. JAMA Surg. 155(2), 148\u2013158 (2020)","journal-title":"JAMA Surg."},{"key":"47_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102306","volume":"76","author":"L Maier-Hein","year":"2022","unstructured":"Maier-Hein, L., et al.: Surgical data science-from concepts toward clinical translation. Med. Image Anal. 76, 102306 (2022)","journal-title":"Med. Image Anal."},{"issue":"9","key":"47_CR22","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1038\/s41551-017-0132-7","volume":"1","author":"L Maier-Hein","year":"2017","unstructured":"Maier-Hein, L., et al.: Surgical data science for next-generation interventions. Nat. Biomed. Eng. 1(9), 691\u2013696 (2017)","journal-title":"Nat. Biomed. Eng."},{"key":"47_CR23","doi-asserted-by":"crossref","unstructured":"Mohamed, A., Qian, K., Elhoseiny, M., Claudel, C.: Social-STGCNN: a social spatio-temporal graph convolutional neural network for human trajectory prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14424\u201314432 (2020)","DOI":"10.1109\/CVPR42600.2020.01443"},{"key":"47_CR24","doi-asserted-by":"crossref","unstructured":"Qin, Y., Feyzabadi, S., Allan, M., Burdick, J.W., Azizian, M.: Davincinet: joint prediction of motion and surgical state in robot-assisted surgery. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems, pp. 2921\u20132928. IEEE (2020)","DOI":"10.1109\/IROS45743.2020.9340723"},{"key":"47_CR25","unstructured":"Ren, A., Veer, S., Majumdar, A.: Generalization guarantees for imitation learning. In: Conference on Robot Learning, pp. 1426\u20131442. PMLR (2021)"},{"key":"47_CR26","doi-asserted-by":"crossref","unstructured":"Sun, J., Jiang, Q., Lu, C.: Recursive social behavior graph for trajectory prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 660\u2013669 (2020)","DOI":"10.1109\/CVPR42600.2020.00074"},{"key":"47_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102291","volume":"75","author":"J Wang","year":"2022","unstructured":"Wang, J., et al.: Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network. Med. Image Anal. 75, 102291 (2022)","journal-title":"Med. Image Anal."},{"key":"47_CR28","doi-asserted-by":"crossref","unstructured":"Wang, Y., Long, Y., Fan, S.H., Dou, Q.: Neural rendering for stereo 3D reconstruction of deformable tissues in robotic surgery. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 431\u2013441 (2022)","DOI":"10.1007\/978-3-031-16449-1_41"},{"issue":"6","key":"47_CR29","doi-asserted-by":"publisher","DOI":"10.1002\/rcs.2441","volume":"18","author":"Z Wang","year":"2022","unstructured":"Wang, Z., Yan, Z., Xing, Y., Wang, H.: Real-time trajectory prediction of laparoscopic instrument tip based on long short-term memory neural network in laparoscopic surgery training. Int. J. Med. Robot. Comput. Assist. Surg. 18(6), e2441 (2022)","journal-title":"Int. J. Med. Robot. Comput. Assist. Surg."},{"key":"47_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1007\/978-3-030-59716-0_39","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"J Zhang","year":"2020","unstructured":"Zhang, J., et al.: Symmetric dilated convolution for surgical gesture recognition. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 409\u2013418. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59716-0_39"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43996-4_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,4]],"date-time":"2024-07-04T16:09:06Z","timestamp":1720109346000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43996-4_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439957","9783031439964"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43996-4_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","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":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","order":11,"name":"conference_url","label":"Conference URL","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","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":"730","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":"32% - 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)"}}]}}