{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T01:59:06Z","timestamp":1743127146851,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031439865"},{"type":"electronic","value":"9783031439872"}],"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\/978-3-031-43987-2_66","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:07:48Z","timestamp":1696115268000},"page":"683-692","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Histopathology Image Classification Using Deep Manifold Contrastive Learning"],"prefix":"10.1007","author":[{"given":"Jing Wei","family":"Tan","sequence":"first","affiliation":[]},{"given":"Won-Ki","family":"Jeong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"66_CR1","unstructured":"Pathology AI platform (PAIP). http:\/\/wisepaip.org\/paip"},{"key":"66_CR2","doi-asserted-by":"crossref","unstructured":"Aziere, N., Todorovic, S.: Ensemble deep manifold similarity learning using hard proxies. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7299\u20137307 (2019)","DOI":"10.1109\/CVPR.2019.00747"},{"key":"66_CR3","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"issue":"10","key":"66_CR4","doi-asserted-by":"publisher","first-page":"10430","DOI":"10.1109\/TCYB.2021.3069790","volume":"52","author":"Z Gong","year":"2021","unstructured":"Gong, Z., Hu, W., Du, X., Zhong, P., Hu, P.: Deep manifold embedding for hyperspectral image classification. IEEE Trans. Cybern. 52(10), 10430\u201310443 (2021)","journal-title":"IEEE Trans. Cybern."},{"key":"66_CR5","doi-asserted-by":"crossref","unstructured":"Guo, Y., et al.: HCSC: hierarchical contrastive selective coding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9706\u20139715 (2022)","DOI":"10.1109\/CVPR52688.2022.00948"},{"key":"66_CR6","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"},{"issue":"7","key":"66_CR7","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1038\/s41591-019-0462-y","volume":"25","author":"JN Kather","year":"2019","unstructured":"Kather, J.N., et al.: Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat. Med. 25(7), 1054\u20131056 (2019)","journal-title":"Nat. Med."},{"issue":"3","key":"66_CR8","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1016\/j.eng.2019.09.010","volume":"6","author":"N Lei","year":"2020","unstructured":"Lei, N.: A geometric understanding of deep learning. Engineering 6(3), 361\u2013374 (2020)","journal-title":"Engineering"},{"key":"66_CR9","unstructured":"Li, J., Zhou, P., Xiong, C., Hoi, S.C.: Prototypical contrastive learning of unsupervised representations. arXiv preprint arXiv:2005.04966 (2020)"},{"key":"66_CR10","doi-asserted-by":"crossref","unstructured":"Lu, J., Wang, G., Deng, W., Moulin, P., Zhou, J.: Multi-manifold deep metric learning for image set classification. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 1137\u20131145 (2015)","DOI":"10.1109\/CVPR.2015.7298717"},{"key":"66_CR11","unstructured":"Ommer, B.: Introduction to similarity and deep metric learning (2022). https:\/\/dvsml2022-tutorial.github.io\/Talks\/02_DML_tutorial_BjornOmmer_.pdf"},{"key":"66_CR12","doi-asserted-by":"crossref","unstructured":"Theodoridis, S., Koutroumbas, K.: Feature generation i: data transformation and dimensionality reduction. Pattern Recogn. 4 (2009)","DOI":"10.1016\/B978-1-59749-272-0.50008-6"},{"key":"66_CR13","doi-asserted-by":"crossref","unstructured":"Zheng, W., Chen, Z., Lu, J., Zhou, J.: Hardness-aware deep metric learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 72\u201381 (2019)","DOI":"10.1109\/CVPR.2019.00016"},{"issue":"7697","key":"66_CR14","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1038\/nature25988","volume":"555","author":"B Zhu","year":"2018","unstructured":"Zhu, B., Liu, J.Z., Cauley, S.F., Rosen, B.R., Rosen, M.S.: Image reconstruction by domain-transform manifold learning. Nature 555(7697), 487\u2013492 (2018)","journal-title":"Nature"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43987-2_66","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T15:34:34Z","timestamp":1710171274000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43987-2_66"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439865","9783031439872"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43987-2_66","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":"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":"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":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","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":"730","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":"32% - 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)"}}]}}