{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T06:27:36Z","timestamp":1759991256394,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031445200"},{"type":"electronic","value":"9783031445217"}],"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\/978-3-031-44521-7_7","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T19:01:58Z","timestamp":1696100518000},"page":"68-78","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Leveraging Self-supervised Learning for\u00a0Fetal Cardiac Planes Classification Using Ultrasound Scan Videos"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4680-9134","authenticated-orcid":false,"given":"Joseph Geo","family":"Benjamin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9461-2749","authenticated-orcid":false,"given":"Mothilal","family":"Asokan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4636-8655","authenticated-orcid":false,"given":"Amna","family":"Alhosani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5404-8849","authenticated-orcid":false,"given":"Hussain","family":"Alasmawi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Werner Gerhard","family":"Diehl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leanne","family":"Bricker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6274-9725","authenticated-orcid":false,"given":"Karthik","family":"Nandakumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6896-1105","authenticated-orcid":false,"given":"Mohammad","family":"Yaqub","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"7_CR1","unstructured":"Bardes, A., Ponce, J., LeCun, Y.: VICReg: variance-invariance-covariance regularization for self-supervised learning. In: International Conference on Learning Representations (2022). https:\/\/openreview.net\/forum?id=xm6YD62D1Ub"},{"issue":"11","key":"7_CR2","doi-asserted-by":"publisher","first-page":"2204","DOI":"10.1109\/TMI.2017.2712367","volume":"36","author":"CF Baumgartner","year":"2017","unstructured":"Baumgartner, C.F., et al.: Sononet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound. IEEE Trans. Med. Imaging 36(11), 2204\u20132215 (2017). https:\/\/doi.org\/10.1109\/TMI.2017.2712367","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"7_CR3","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1002\/uog.26224","volume":"61","author":"JS Carvalho","year":"2023","unstructured":"Carvalho, J.S., et al.: Isuog practice guidelines (updated): fetal cardiac screening. Ultrasound Obstetr. Gynecol. 61(6), 788\u2013803 (2023). https:\/\/doi.org\/10.1002\/uog.26224","journal-title":"Ultrasound Obstetr. Gynecol."},{"key":"7_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101539","volume":"58","author":"L Chen","year":"2019","unstructured":"Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., Rueckert, D.: Self-supervised learning for medical image analysis using image context restoration. Med. Image Anal. 58, 101539 (2019). https:\/\/doi.org\/10.1016\/j.media.2019.101539","journal-title":"Med. Image Anal."},{"key":"7_CR5","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: Proceedings of the 37th International Conference on Machine Learning. ICML\u201920. JMLR.org (2020). https:\/\/dl.acm.org\/doi\/abs\/10.5555\/3524938.3525087"},{"key":"7_CR6","doi-asserted-by":"publisher","unstructured":"Dadoun, H., Delingette, H., Rousseau, A.L., Kerviler, E.d., Ayache, N.: Combining Bayesian and deep learning methods for the delineation of the fan in ultrasound images. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 743\u2013747 (2021). https:\/\/doi.org\/10.1109\/ISBI48211.2021.9434112","DOI":"10.1109\/ISBI48211.2021.9434112"},{"key":"7_CR7","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: 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":"7_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102629","volume":"83","author":"MC Fiorentino","year":"2023","unstructured":"Fiorentino, M.C., Villani, F.P., Di Cosmo, M., Frontoni, E., Moccia, S.: A review on deep-learning algorithms for fetal ultrasound-image analysis. Med. Image Anal. 83, 102629 (2023). https:\/\/doi.org\/10.1016\/j.media.2022.102629","journal-title":"Med. Image Anal."},{"key":"7_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1007\/978-3-031-25066-8_23","volume-title":"Computer Vision - ECCV 2022 Workshops","author":"Z Fu","year":"2023","unstructured":"Fu, Z., Jiao, J., Yasrab, R., Drukker, L., Papageorghiou, A.T., Noble, J.A.: Anatomy-aware contrastive representation learning for fetal ultrasound. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) ECCV 2022. LNCS, vol. 13803, pp. 422\u2013436. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-25066-8_23"},{"key":"7_CR10","unstructured":"Grill, J.B., et al.: Bootstrap your own latent a new approach to self-supervised learning. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS\u201920. Curran Associates Inc., Red Hook (2020). https:\/\/dl.acm.org\/doi\/abs\/10.5555\/3495724.3497510"},{"key":"7_CR11","doi-asserted-by":"publisher","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9726\u20139735 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00975","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"7_CR12","unstructured":"Holste, G., Oikonomou, E.K., Mortazavi, B.J., Wang, Z., Khera, R.: Self-supervised learning of echocardiogram videos enables data-efficient clinical diagnosis. arXiv abs\/2207.11581 (2022). https:\/\/api.semanticscholar.org\/CorpusID:251040927"},{"key":"7_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-87722-4_1","volume-title":"Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health","author":"MR Hosseinzadeh Taher","year":"2021","unstructured":"Hosseinzadeh Taher, M.R., Haghighi, F., Feng, R., Gotway, M.B., Liang, J.: A systematic benchmarking analysis of\u00a0transfer learning for medical image\u00a0analysis. In: Albarqouni, S., et al. (eds.) DART\/FAIR -2021. LNCS, vol. 12968, pp. 3\u201313. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87722-4_1"},{"key":"7_CR14","doi-asserted-by":"publisher","unstructured":"Jiao, J., Droste, R., Drukker, L., Papageorghiou, A.T., Noble, J.A.: Self-supervised representation learning for ultrasound video. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1847\u20131850 (2020). https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098666","DOI":"10.1109\/ISBI45749.2020.9098666"},{"key":"7_CR15","doi-asserted-by":"publisher","unstructured":"Kornblith, S., Shlens, J., Le, Q.V.: Do better imagenet models transfer better? In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2656\u20132666. IEEE Computer Society, Los Alamitos, CA, USA (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00277","DOI":"10.1109\/CVPR.2019.00277"},{"key":"7_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/978-3-642-21735-7_7","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2011","author":"J Masci","year":"2011","unstructured":"Masci, J., Meier, U., Cire\u015fan, D., Schmidhuber, J.: Stacked convolutional auto-encoders for hierarchical feature extraction. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds.) ICANN 2011. LNCS, vol. 6791, pp. 52\u201359. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-21735-7_7"},{"key":"7_CR17","unstructured":"NHS-England: Fetal anomaly screening programme handbook: 20-week screening scan, 4 May 2023. https:\/\/www.gov.uk\/government\/publications\/fetal-anomaly-screening-programme-handbook\/20-week-screening-scan"},{"key":"7_CR18","doi-asserted-by":"publisher","unstructured":"Pathak, D., Kr\u00e4henb\u00fchl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: Feature learning by inpainting. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2536\u20132544 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.278","DOI":"10.1109\/CVPR.2016.278"},{"key":"7_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1007\/978-3-031-12053-4_50","volume-title":"Medical Image Understanding and Analysis - MIUA 2022","author":"M Saeed","year":"2022","unstructured":"Saeed, M., Muhtaseb, R., Yaqub, M.: Contrastive pretraining for echocardiography segmentation with limited data. In: Yang, G., Aviles-Rivero, A., Roberts, M., Sch\u00f6nlieb, C.B. (eds.) MIUA 2022. LNCS, vol. 13413, pp. 680\u2013691. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-12053-4_50"},{"key":"7_CR20","doi-asserted-by":"publisher","unstructured":"Schiappa, M.C., Rawat, Y.S., Shah, M.: Self-supervised learning for videos: a survey. ACM Comput. Surv. 55(13s) (2023). https:\/\/doi.org\/10.1145\/3577925","DOI":"10.1145\/3577925"},{"key":"7_CR21","doi-asserted-by":"publisher","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 618\u2013626 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.74","DOI":"10.1109\/ICCV.2017.74"},{"key":"7_CR22","unstructured":"Shwartz-Ziv, R., Balestriero, R., LeCun, Y.: What do we maximize in self-supervised learning? In: First Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward at ICML 2022 (2022). https:\/\/openreview.net\/forum?id=FChTGTaVcc"},{"key":"7_CR23","unstructured":"Tian, Y., Sun, C., Poole, B., Krishnan, D., Schmid, C., Isola, P.: What makes for good views for contrastive learning? In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS\u201920. Curran Associates Inc., Red Hook, NY, USA (2020). https:\/\/dl.acm.org\/doi\/10.5555\/3495724.3496297"},{"issue":"5","key":"7_CR24","doi-asserted-by":"publisher","first-page":"1336","DOI":"10.1109\/TCYB.2017.2671898","volume":"47","author":"L Wu","year":"2017","unstructured":"Wu, L., Cheng, J.Z., Li, S., Lei, B., Wang, T., Ni, D.: Fuiqa: fetal ultrasound image quality assessment with deep convolutional networks. IEEE Trans. Cybern. 47(5), 1336\u20131349 (2017). https:\/\/doi.org\/10.1109\/TCYB.2017.2671898","journal-title":"IEEE Trans. Cybern."},{"key":"7_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1007\/978-3-319-24574-4_82","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"M Yaqub","year":"2015","unstructured":"Yaqub, M., Kelly, B., Papageorghiou, A.T., Noble, J.A.: Guided random forests for identification of key fetal anatomy and image categorization in ultrasound scans. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 687\u2013694. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_82"},{"key":"7_CR26","unstructured":"Zbontar, J., Jing, L., Misra, I., LeCun, Y., Deny, S.: Barlow twins: self-supervised learning via redundancy reduction. In: Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 139, pp. 12310\u201312320. PMLR, 18\u201324 July 2021. https:\/\/proceedings.mlr.press\/v139\/zbontar21a.html"},{"key":"7_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-031-26351-4_1","volume-title":"Computer Vision - ACCV 2022","author":"C Zhang","year":"2023","unstructured":"Zhang, C., Chen, Y., Liu, L., Liu, Q., Zhou, X.: Hico: hierarchical contrastive learning for ultrasound video model pretraining. In: Wang, L., Gall, J., Chin, T.J., Sato, I., Chellappa, R. (eds.) ACCV 2022. LNCS, vol. 13846, pp. 3\u201320. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-26351-4_1"}],"container-title":["Lecture Notes in Computer Science","Simplifying Medical Ultrasound"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44521-7_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T19:02:20Z","timestamp":1696100540000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44521-7_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031445200","9783031445217"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44521-7_7","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":"ASMUS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Advances in Simplifying Medical Ultrasound","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":"8 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"asmus2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai-ultrasound.github.io\/#\/asmus23","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":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30","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":"19","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":"63% - 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":"3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}