{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T04:46:36Z","timestamp":1758343596885,"version":"3.44.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051264","type":"print"},{"value":"9783032051271","type":"electronic"}],"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\/978-3-032-05127-1_46","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T21:16:33Z","timestamp":1758316593000},"page":"478-488","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SemiVT-Surge: Semi-supervised Video Transformer for\u00a0Surgical Phase Recognition"],"prefix":"10.1007","author":[{"given":"Yiping","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronald","family":"de Jong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sahar","family":"Nasirihaghighi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tim","family":"Jaspers","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Romy","family":"van Jaarsveld","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gino","family":"Kuiper","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"van Hillegersberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fons","family":"van der Sommen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jelle","family":"Ruurda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcel","family":"Breeuwer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasmina","family":"Al Khalil","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"46_CR1","doi-asserted-by":"crossref","unstructured":"Alapatt, D., Murali, A., Srivastav, V., Consortium, A., Mascagni, P., Padoy, N.: Jumpstarting surgical computer vision. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 328\u2013338. Springer (2024)","DOI":"10.1007\/978-3-031-72089-5_31"},{"key":"46_CR2","unstructured":"Author, A.: Paper title. In: This paper is accepted by XXX but not yet available to the public. More details on the dataset are provided in this work. (2025)"},{"key":"46_CR3","doi-asserted-by":"crossref","unstructured":"Basak, H., Yin, Z.: Pseudo-label guided contrastive learning for semi-supervised medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19786\u201319797 (2023)","DOI":"10.1109\/CVPR52729.2023.01895"},{"issue":"6","key":"46_CR4","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1007\/s11548-024-03091-5","volume":"19","author":"D Bati\u0107","year":"2024","unstructured":"Bati\u0107, D., Holm, F., \u00d6zsoy, E., Czempiel, T., Navab, N.: Endovit: pretraining vision transformers on a large collection of endoscopic images. Int. J. Comput. Assisted Radiol. Surgery 19(6), 1085\u20131091 (2024)","journal-title":"Int. J. Comput. Assisted Radiol. Surgery"},{"key":"46_CR5","unstructured":"Bertasius, G., Wang, H., Torresani, L.: Is space-time attention all you need for video understanding? In: ICML. vol.\u00a02, p.\u00a04 (2021)"},{"key":"46_CR6","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: Autoaugment: Learning augmentation strategies from data. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 113\u2013123 (2019)","DOI":"10.1109\/CVPR.2019.00020"},{"key":"46_CR7","doi-asserted-by":"publisher","unstructured":"Czempiel, T., Paschali, M., Keicher, M., Simson, W., Feussner, H., Kim, S.T., Navab, N.: 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","DOI":"10.1007\/978-3-030-59716-0_33"},{"key":"46_CR8","doi-asserted-by":"crossref","unstructured":"Dave, I.R., Rizve, M.N., Chen, C., Shah, M.: Timebalance: Temporally-invariant and temporally-distinctive video representations for semi-supervised action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2341\u20132352 (2023)","DOI":"10.1109\/CVPR52729.2023.00232"},{"key":"46_CR9","unstructured":"Funke, I., Rivoir, D., Speidel, S.: Metrics matter in surgical phase recognition. arXiv preprint arXiv:2305.13961 (2023)"},{"key":"46_CR10","doi-asserted-by":"crossref","unstructured":"Guan, J., Zou, X., Tao, R., Zheng, G.: Label-guided teacher for surgical phase recognition via knowledge distillation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 349\u2013358. Springer (2024)","DOI":"10.1007\/978-3-031-72089-5_33"},{"key":"46_CR11","doi-asserted-by":"crossref","unstructured":"He, A., Li, T., Zhao, Y., Zhao, J., Fu, H.: Open-set semi-supervised medical image classification with learnable prototypes and outlier filter. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 492\u2013501. Springer (2024)","DOI":"10.1007\/978-3-031-72120-5_46"},{"key":"46_CR12","doi-asserted-by":"crossref","unstructured":"Jaspers, T.J., et\u00a0al.: Exploring the effect of dataset diversity in self-supervised learning for surgical computer vision. In: MICCAI Workshop on Data Engineering in Medical Imaging, pp. 43\u201353. Springer (2024)","DOI":"10.1007\/978-3-031-73748-0_5"},{"key":"46_CR13","unstructured":"Jaspers, T.J., et\u00a0al.: Scaling up self-supervised learning for improved surgical foundation models. arXiv preprint arXiv:2501.09436 (2025)"},{"issue":"7","key":"46_CR14","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1109\/TMI.2022.3222126","volume":"42","author":"H Kassem","year":"2022","unstructured":"Kassem, H., Alapatt, D., Mascagni, P., Karargyris, A., Padoy, N.: Federated cycling (fedcy): semi-supervised federated learning of surgical phases. IEEE Trans. Med. Imaging 42(7), 1920\u20131931 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"46_CR15","unstructured":"Kay, W., et\u00a0al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017)"},{"key":"46_CR16","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Skit: a fast key information video transformer for online surgical phase recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 21074\u201321084 (2023)","DOI":"10.1109\/ICCV51070.2023.01927"},{"key":"46_CR17","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 Analy. 76, 102306 (2022)","journal-title":"Med. Image Analy."},{"key":"46_CR18","doi-asserted-by":"crossref","unstructured":"P\u00e9rez, A., Rodr\u00edguez, S., Ayobi, N., Aparicio, N., Dessevres, E., Arbel\u00e1ez, P.: Must: multi-scale t ransformers for surgical phase recognition. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 422\u2013432. Springer (2024)","DOI":"10.1007\/978-3-031-72089-5_40"},{"key":"46_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102158","volume":"73","author":"X Shi","year":"2021","unstructured":"Shi, X., Jin, Y., Dou, Q., Heng, P.A.: Semi-supervised learning with progressive unlabeled data excavation for label-efficient surgical workflow recognition. Med. Image Anal. 73, 102158 (2021)","journal-title":"Med. Image Anal."},{"key":"46_CR20","first-page":"596","volume":"33","author":"K Sohn","year":"2020","unstructured":"Sohn, K., et al.: Fixmatch: simplifying semi-supervised learning with consistency and confidence. Adv. Neural. Inf. Process. Syst. 33, 596\u2013608 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"46_CR21","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"issue":"1","key":"46_CR22","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., 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"},{"key":"46_CR23","doi-asserted-by":"crossref","unstructured":"Wang, Z., Liu, C., Zhang, S., Dou, Q.: Foundation model for endoscopy video analysis via large-scale self-supervised pre-train. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 101\u2013111. Springer (2023)","DOI":"10.1007\/978-3-031-43996-4_10"},{"key":"46_CR24","doi-asserted-by":"crossref","unstructured":"Xing, Z., Dai, Q., Hu, H., Chen, J., Wu, Z., Jiang, Y.G.: Svformer: Semi-supervised video transformer for action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18816\u201318826 (2023)","DOI":"10.1109\/CVPR52729.2023.01804"},{"key":"46_CR25","doi-asserted-by":"crossref","unstructured":"Xu, Y., et al.: Cross-model pseudo-labeling for semi-supervised action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2959\u20132968 (2022)","DOI":"10.1109\/CVPR52688.2022.00297"},{"key":"46_CR26","doi-asserted-by":"crossref","unstructured":"Yang, S., Luo, L., Wang, Q., Chen, H.: Surgformer: surgical transformer with hierarchical temporal attention for surgical phase recognition. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 606\u2013616. Springer (2024)","DOI":"10.1007\/978-3-031-72089-5_57"}],"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\/978-3-032-05127-1_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T21:16:41Z","timestamp":1758316601000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05127-1_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032051264","9783032051271"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05127-1_46","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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 to declare that are relevant to the content of this article.","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"}}]}}