{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T06:54:19Z","timestamp":1758351259427,"version":"3.44.0"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032051400"},{"type":"electronic","value":"9783032051417"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-05141-7_18","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:18:29Z","timestamp":1758269909000},"page":"178-188","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Feature Mixing Approach for\u00a0Detecting Intraoperative Adverse Events in\u00a0Laparoscopic Roux-en-Y Gastric Bypass Surgery"],"prefix":"10.1007","author":[{"given":"Rupak","family":"Bose","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4777-0857","authenticated-orcid":false,"given":"Chinedu Innocent","family":"Nwoye","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5508-0747","authenticated-orcid":false,"given":"Jorge F.","family":"Lazo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0248-4996","authenticated-orcid":false,"given":"Jo\u00ebl L.","family":"Lavanchy","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5010-4137","authenticated-orcid":false,"given":"Nicolas","family":"Padoy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Audibert, J., Michiardi, P., Guyard, F., Marti, S., Zuluaga, M.A.: USAD: unsupervised anomaly detection on multivariate time series. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3395\u20133404 (2020)","DOI":"10.1145\/3394486.3403392"},{"key":"18_CR2","doi-asserted-by":"crossref","unstructured":"Beyersdorffer, P., et al.: Detection of adverse events leading to inadvertent injury during laparoscopic cholecystectomy using convolutional neural networks. Biomed. Eng.\/Biomedizinische Technik 66(4), 413\u2013421 (2021)","DOI":"10.1515\/bmt-2020-0106"},{"key":"18_CR3","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"issue":"23","key":"18_CR4","doi-asserted-by":"publisher","first-page":"7355","DOI":"10.3390\/jcm12237355","volume":"12","author":"E Checcucci","year":"2023","unstructured":"Checcucci, E., et al.: Development of bleeding artificial intelligence detector (BLAIR) system for robotic radical prostatectomy. J. Clin. Med. 12(23), 7355 (2023)","journal-title":"J. Clin. Med."},{"key":"18_CR5","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"issue":"4","key":"18_CR6","doi-asserted-by":"publisher","first-page":"1687","DOI":"10.3390\/jcm12041687","volume":"12","author":"MB Eppler","year":"2023","unstructured":"Eppler, M.B., et al.: Automated capture of intraoperative adverse events using artificial intelligence: a systematic review and meta-analysis. J. Clin. Med. 12(4), 1687 (2023)","journal-title":"J. Clin. Med."},{"issue":"6","key":"18_CR7","doi-asserted-by":"publisher","first-page":"1570","DOI":"10.1016\/j.surg.2021.12.011","volume":"171","author":"L Gawria","year":"2022","unstructured":"Gawria, L., et al.: Classification of intraoperative adverse events in visceral surgery. Surgery 171(6), 1570\u20131579 (2022)","journal-title":"Surgery"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Hua, S., et\u00a0al.: Automatic bleeding detection in laparoscopic surgery based on a faster region-based convolutional neural network. Ann. Transl. Med. 10(10) (2022)","DOI":"10.21037\/atm-22-1914"},{"issue":"2","key":"18_CR10","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1097\/SLA.0000000000003897","volume":"272","author":"JJ Jung","year":"2020","unstructured":"Jung, J.J., et al.: Development and evaluation of a novel instrument to measure severity of intraoperative events using video data. Ann. Surg. 272(2), 220\u2013226 (2020)","journal-title":"Ann. Surg."},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Kawachi, Y., Koizumi, Y., Harada, N.: Complementary set variational autoencoder for supervised anomaly detection. In: 2018 ICASSP, pp. 2366\u20132370. IEEE (2018)","DOI":"10.1109\/ICASSP.2018.8462181"},{"key":"18_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/s00464-025-11557-z","author":"JL Lavanchy","year":"2025","unstructured":"Lavanchy, J.L., et al.: Analyzing the impact of surgical technique on intraoperative adverse events in laparoscopic Roux-en-Y gastric bypass surgery by video-based assessment. Surg. Endosc. (2025). https:\/\/doi.org\/10.1007\/s00464-025-11557-z","journal-title":"Surg. Endosc."},{"key":"18_CR13","unstructured":"Lavanchy, J.L., et al.: Challenges in multi-centric generalization: phase and step recognition in Roux-en-Y gastric bypass surgery. Int. J. Comput. Assist. Radiol. Surgery 1\u20139 (2024)"},{"key":"18_CR14","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: ICCV, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"issue":"1","key":"18_CR15","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1109\/TAFFC.2015.2508454","volume":"8","author":"S Mariooryad","year":"2015","unstructured":"Mariooryad, S., Busso, C.: The cost of dichotomizing continuous labels for binary classification problems: deriving a Bayesian-optimal classifier. IEEE Trans. Affect. Comput. 8(1), 119\u2013130 (2015)","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"2","key":"18_CR16","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1136\/bmjqs-2015-004405","volume":"25","author":"I Mitchell","year":"2016","unstructured":"Mitchell, I., Schuster, A., Smith, K., Pronovost, P., Wu, A.: Patient safety incident reporting: a qualitative study of thoughts and perceptions of experts 15 years after \u2018to err is human\u2019. BMJ Qual. Saf. 25(2), 92\u201399 (2016)","journal-title":"BMJ Qual. Saf."},{"key":"18_CR17","unstructured":"Nwoye, C.I.: Deep learning methods for the detection and recognition of surgical tools and activities in laparoscopic videos. Ph.D. thesis, Universit\u00e9 de Strasbourg (2021)"},{"key":"18_CR18","unstructured":"Ruff, L., et al.: Deep semi-supervised anomaly detection. In: International Conference on Learning Representations (2020). https:\/\/openreview.net\/forum?id=HkgH0TEYwH"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: CVPR, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"18_CR20","unstructured":"Wei, H., Rudzicz, F., Fleet, D., Grantcharov, T., Taati, B.: Intraoperative adverse event detection in laparoscopic surgery: stabilized multi-stage temporal convolutional network with focal-uncertainty loss. In: Machine Learning for Healthcare Conference, pp. 283\u2013307. PMLR (2021)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05141-7_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:18:37Z","timestamp":1758269917000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05141-7_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032051400","9783032051417"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05141-7_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests in the paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}