{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T17:58:51Z","timestamp":1774375131172,"version":"3.50.1"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031737473","type":"print"},{"value":"9783031737480","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73748-0_5","type":"book-chapter","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T19:02:33Z","timestamp":1729796553000},"page":"43-53","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Exploring the\u00a0Effect of\u00a0Dataset Diversity in\u00a0Self-supervised Learning for\u00a0Surgical Computer Vision"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8306-5058","authenticated-orcid":false,"given":"Tim J. M.","family":"Jaspers","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7806-4340","authenticated-orcid":false,"given":"Ronald L. P. D.","family":"de Jong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasmina","family":"Al Khalil","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tijn","family":"Zeelenberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3114-3888","authenticated-orcid":false,"given":"Carolus H. J.","family":"Kusters","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiping","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Romy C.","family":"van Jaarsveld","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Franciscus H. A.","family":"Bakker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jelle P.","family":"Ruurda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Willem M.","family":"Brinkman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter H. N.","family":"De\u00a0With","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3593-2356","authenticated-orcid":false,"given":"Fons","family":"van der Sommen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Alapatt, D., Murali, A., Srivastav, V., Mascagni, P., Consortium, A., Padoy, N.: Jumpstarting surgical computer vision (2023)","DOI":"10.1007\/978-3-031-72089-5_31"},{"key":"5_CR2","doi-asserted-by":"publisher","unstructured":"Bakker, F.H.A., de\u00a0Nijs, J.V., Jaspers, T., et\u00a0al.: Estimating surgical urethral length on intraoperative robot-assisted prostatectomy images using artificial intelligence anatomy recognition. J. Endourol. 38(7), 690\u2013696 (2024). https:\/\/doi.org\/10.1089\/end.2023.0697, pMID: 38613819","DOI":"10.1089\/end.2023.0697"},{"key":"5_CR3","unstructured":"Bawa, V.S., Singh, G., KapingA, F., et\u00a0al.: The SARAS Endoscopic Surgeon Action Detection (ESAD) dataset: challenges and methods (2021)"},{"issue":"7","key":"5_CR4","doi-asserted-by":"publisher","first-page":"5164","DOI":"10.1007\/s00464-023-09990-z","volume":"37","author":"RB den Boer","year":"2023","unstructured":"den Boer, R.B., Jaspers, T.J.M., de Jongh, C., et al.: Deep learning-based recognition of key anatomical structures during robot-assisted minimally invasive esophagectomy. Surg. Endosc. 37(7), 5164\u20135175 (2023). https:\/\/doi.org\/10.1007\/s00464-023-09990-z","journal-title":"Surg. Endosc."},{"issue":"12","key":"5_CR5","doi-asserted-by":"publisher","first-page":"8737","DOI":"10.1007\/s00464-022-09421-5","volume":"36","author":"RB den Boer","year":"2022","unstructured":"den Boer, R.B., de Jongh, C., Huijbers, W.T.E., et al.: Computer-aided anatomy recognition in intrathoracic and -abdominal surgery: a systematic review. Surg. Endosc. 36(12), 8737\u20138752 (2022). https:\/\/doi.org\/10.1007\/s00464-022-09421-5","journal-title":"Surg. Endosc."},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Caron, M., Touvron, H., et\u00a0al.: Emerging properties in self-supervised vision transformers. In: Proceedings of the International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"5_CR7","doi-asserted-by":"publisher","unstructured":"Carstens, M., Rinner, F.M., Bodenstedt, S., et al.: The Dresden surgical anatomy dataset for abdominal organ segmentation in surgical data science. Sci. Data 10(1), 3 (2023). https:\/\/doi.org\/10.1038\/s41597-022-01719-2","DOI":"10.1038\/s41597-022-01719-2"},{"key":"5_CR8","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., et\u00a0al.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"5_CR9","unstructured":"Dosovitskiy, A., et al.: An image is worth 16\u00a0$$\\times $$\u00a016 words: transformers for image recognition at scale (2021)"},{"key":"5_CR10","doi-asserted-by":"publisher","unstructured":"Hashimoto, D.A., Rosman, G., Volkov, M., Rus, D.L., Meireles, O.R.: Artificial intelligence for intraoperative video analysis: machine learning\u2019s role in surgical education. J. Am. Coll. Surg. 225(4, Suppl. 1), S171 (2017). https:\/\/doi.org\/10.1016\/j.jamcollsurg.2017.07.387, Scientific Forum Abstracts: 2017 Clinical Congress","DOI":"10.1016\/j.jamcollsurg.2017.07.387"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"5_CR12","doi-asserted-by":"publisher","unstructured":"Hirsch, R., Caron, M., Cohen, R., et\u00a0al.: Self-supervised learning for endoscopic video analysis. In: Greenspan, H., et al. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023, pp. 569\u2013578. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43904-9_55","DOI":"10.1007\/978-3-031-43904-9_55"},{"key":"5_CR13","unstructured":"Hong, W.Y., Kao, C.L., Kuo, Y.H., et\u00a0al.: CholecSeg8k: a semantic segmentation dataset for laparoscopic cholecystectomy based on Cholec80 (2020)"},{"key":"5_CR14","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Girshick, R., He, K., Dollar, P.: Panoptic feature pyramid networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019","DOI":"10.1109\/CVPR.2019.00656"},{"key":"5_CR15","doi-asserted-by":"publisher","unstructured":"Lavanchy, J.L., Ramesh, S., Dall\u2019Alba, D., et\u00a0al.: Challenges in multi-centric generalization: phase and step recognition in Roux-en-Y gastric bypass surgery. Int. J. Comput. Assist. Radiol. Surg. (2024). https:\/\/doi.org\/10.1007\/s11548-024-03166-3","DOI":"10.1007\/s11548-024-03166-3"},{"key":"5_CR16","doi-asserted-by":"publisher","unstructured":"Leibetseder, A., Kletz, S., Schoeffmann, K., Keckstein, S., Keckstein, J.: GLENDA: gynecologic laparoscopy endometriosis dataset. In: Ro, Y.M., et al. (eds.) MultiMedia Modeling, pp. 439\u2013450. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-37734-2_36","DOI":"10.1007\/978-3-030-37734-2_36"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Leibetseder, A., Petscharnig, S., Primus, M.J., et\u00a0al.: LapGyn4: a dataset for 4 automatic content analysis problems in the domain of laparoscopic gynecology. In: Proceedings of the 9th ACM Multimedia Systems Conference, pp. 357\u2013362 (2018)","DOI":"10.1145\/3204949.3208127"},{"key":"5_CR18","doi-asserted-by":"publisher","first-page":"102306","DOI":"10.1016\/j.media.2021.102306","volume":"76","author":"L Maier-Hein","year":"2022","unstructured":"Maier-Hein, L., Eisenmann, M., Sarikaya, D., et al.: Surgical data science - from concepts toward clinical translation. Med. Image Anal. 76, 102306 (2022). https:\/\/doi.org\/10.1016\/j.media.2021.102306","journal-title":"Med. Image Anal."},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Maier-Hein, L., Wagner, M., Ross, T., et\u00a0al.: Heidelberg colorectal data set for surgical data science in the sensor operating room (2021)","DOI":"10.1038\/s41597-021-00882-2"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Mascagni, P., Vardazaryan, A., Alapatt, D., et\u00a0al.: Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Ann. Surg. 275(5), 955\u2013961 (2022)","DOI":"10.1097\/SLA.0000000000004351"},{"key":"5_CR21","doi-asserted-by":"publisher","unstructured":"Padoy, N., Blum, T., Ahmadi, S.A., Feussner, H., Berger, M.O., Navab, N.: Statistical modeling and recognition of surgical workflow. Med. Image Anal. 16(3), 632\u2013641 (2012). https:\/\/doi.org\/10.1016\/j.media.2010.10.001, Computer Assisted Interventions","DOI":"10.1016\/j.media.2010.10.001"},{"key":"5_CR22","doi-asserted-by":"publisher","first-page":"102844","DOI":"10.1016\/j.media.2023.102844","volume":"88","author":"S Ramesh","year":"2023","unstructured":"Ramesh, S., Srivastav, V., Alapatt, D., et al.: Dissecting self-supervised learning methods for surgical computer vision. Med. Image Anal. 88, 102844 (2023). https:\/\/doi.org\/10.1016\/j.media.2023.102844","journal-title":"Med. Image Anal."},{"key":"5_CR23","unstructured":"Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks (2020)"},{"issue":"1","key":"5_CR24","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1109\/TMI.2016.2593957","volume":"36","author":"AP Twinanda","year":"2017","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 (2017). https:\/\/doi.org\/10.1109\/TMI.2016.2593957","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5_CR25","doi-asserted-by":"publisher","unstructured":"Valderrama, N., Ruiz\u00a0Puentes, P., Hern\u00e1ndez, I., et\u00a0al.: Towards holistic surgical scene understanding. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022, pp. 442\u2013452. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16449-1_42","DOI":"10.1007\/978-3-031-16449-1_42"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: Pyramid vision transformer: a versatile backbone for dense prediction without convolutions (2021)","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"5_CR27","doi-asserted-by":"publisher","unstructured":"Wang, Z., Liu, C., et\u00a0al.: Foundation model for endoscopy video analysis via large-scale self-supervised pre-train. In: Greenspan, H., et al. (eds.) International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 14228, pp. 101\u2013111. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43996-4_10","DOI":"10.1007\/978-3-031-43996-4_10"},{"key":"5_CR28","doi-asserted-by":"publisher","unstructured":"Yoon, J., Lee, J., Heo, S., et\u00a0al.: hSDB-instrument: Instrument localization database for laparoscopic and robotic surgeries. In: de Bruijne, M., et al. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021, pp. 393\u2013402. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87202-1_38","DOI":"10.1007\/978-3-030-87202-1_38"},{"issue":"2","key":"5_CR29","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1109\/tpami.2023.3329173","volume":"46","author":"W Yu","year":"2024","unstructured":"Yu, W., Si, C., Zhou, P., et al.: MetaFormer baselines for vision. IEEE Trans. Pattern Anal. Mach. Intell. 46(2), 896\u2013912 (2024). https:\/\/doi.org\/10.1109\/tpami.2023.3329173","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5_CR30","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Bano, S., Page, A.S., Deprest, J., Stoyanov, D., Vasconcelos, F.: Retrieval of surgical phase transitions using reinforcement learning. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022, pp. 497\u2013506. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16449-1_47","DOI":"10.1007\/978-3-031-16449-1_47"},{"key":"5_CR31","unstructured":"Zia, A., Bhattacharyya, K., Liu, X., et\u00a0al.: Surgical tool classification and localization: results and methods from the MICCAI 2022 SurgToolLoc challenge (2023)"}],"container-title":["Lecture Notes in Computer Science","Data Engineering in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73748-0_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T19:04:11Z","timestamp":1729796651000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73748-0_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,25]]},"ISBN":["9783031737473","9783031737480"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73748-0_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,25]]},"assertion":[{"value":"25 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DEMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Data Engineering in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"demi2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/demi-workshop.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}