{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T15:28:44Z","timestamp":1773329324485,"version":"3.50.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164361","type":"print"},{"value":"9783031164378","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16437-8_68","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T18:13:04Z","timestamp":1663265584000},"page":"707-716","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Vision-Language Contrastive Learning Approach to\u00a0Robust Automatic Placenta Analysis Using Photographic Images"],"prefix":"10.1007","author":[{"given":"Yimu","family":"Pan","sequence":"first","affiliation":[]},{"given":"Alison D.","family":"Gernand","sequence":"additional","affiliation":[]},{"given":"Jeffery A.","family":"Goldstein","sequence":"additional","affiliation":[]},{"given":"Leena","family":"Mithal","sequence":"additional","affiliation":[]},{"given":"Delia","family":"Mwinyelle","sequence":"additional","affiliation":[]},{"given":"James Z.","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"68_CR1","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: Proceedings of the International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"key":"68_CR2","unstructured":"Chen, T., Luo, C., Li, L.: Intriguing properties of contrastive losses. In: Advances in Neural Information Processing Systems, vol. 34 (2021)"},{"key":"68_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1007\/978-3-030-32239-7_54","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Y Chen","year":"2019","unstructured":"Chen, Y., Wu, C., Zhang, Z., Goldstein, J.A., Gernand, A.D., Wang, J.Z.: PlacentaNet: automatic morphological characterization of placenta photos with deep learning. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 487\u2013495. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_54"},{"issue":"101744","key":"68_CR4","first-page":"1","volume":"84","author":"Y Chen","year":"2020","unstructured":"Chen, Y., et al.: AI-PLAX: AI-based placental assessment and examination using photos. Comput. Med. Imaging Graph. 84(101744), 1\u201315 (2020)","journal-title":"Comput. Med. Imaging Graph."},{"key":"68_CR5","unstructured":"Denize, J., Rabarisoa, J., Orcesi, A., H\u00e9rault, R., Canu, S.: Similarity contrastive estimation for self-supervised soft contrastive learning. arXiv preprint arXiv:2111.14585 (2021)"},{"key":"68_CR6","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"issue":"8","key":"68_CR7","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861\u2013874 (2006)","journal-title":"Pattern Recogn. Lett."},{"key":"68_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"68_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"7","key":"68_CR10","doi-asserted-by":"publisher","first-page":"698","DOI":"10.5858\/arpa.2015-0225-CC","volume":"140","author":"TY Khong","year":"2016","unstructured":"Khong, T.Y., et al.: Sampling and definitions of placental lesions: Amsterdam placental workshop group consensus statement. Archi. Pathol. Lab. Med. 140(7), 698\u2013713 (2016)","journal-title":"Archi. Pathol. Lab. Med."},{"key":"68_CR11","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"68_CR12","unstructured":"Li, T., et al.: Addressing feature suppression in unsupervised visual representations. arXiv preprint arXiv:2012.09962 (2020)"},{"key":"68_CR13","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)"},{"key":"68_CR14","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"key":"68_CR15","unstructured":"Rezaei, M., Soleymani, F., Bischl, B., Azizi, S.: Deep bregman divergence for contrastive learning of visual representations. arXiv preprint arXiv:2109.07455 (2021)"},{"key":"68_CR16","unstructured":"Robinson, J., Sun, L., Yu, K., Batmanghelich, K., Jegelka, S., Sra, S.: Can contrastive learning avoid shortcut solutions? arXiv preprint arXiv:2106.11230 (2021)"},{"issue":"56","key":"68_CR17","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(56), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"68_CR18","unstructured":"Zhang, Y., Jiang, H., Miura, Y., Manning, C.D., Langlotz, C.P.: Contrastive learning of medical visual representations from paired images and text. arXiv preprint arXiv:2010.00747 (2020)"},{"key":"68_CR19","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.patrec.2020.10.004","volume":"140","author":"Z Zhang","year":"2020","unstructured":"Zhang, Z., Davaasuren, D., Wu, C., Goldstein, J.A., Gernand, A.D., Wang, J.Z.: Multi-region saliency-aware learning for cross-domain placenta image segmentation. Pattern Recogn. Lett. 140, 165\u2013171 (2020)","journal-title":"Pattern Recogn. Lett."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16437-8_68","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T14:11:37Z","timestamp":1710252697000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16437-8_68"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164361","9783031164378"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16437-8_68","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 2022","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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","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":"Microsoft Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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)"}}]}}