{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:41:48Z","timestamp":1759358508100,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030597092"},{"type":"electronic","value":"9783030597108"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-59710-8_56","type":"book-chapter","created":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T18:06:41Z","timestamp":1601575601000},"page":"572-581","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Improving Dense Pixelwise Prediction of Epithelial Density Using Unsupervised Data Augmentation for Consistency Regularization"],"prefix":"10.1007","author":[{"given":"Minh Nguyen Nhat","family":"To","sequence":"first","affiliation":[]},{"given":"Sandeep","family":"Sankineni","sequence":"additional","affiliation":[]},{"given":"Sheng","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Baris","family":"Turkbey","sequence":"additional","affiliation":[]},{"given":"Peter A.","family":"Pinto","sequence":"additional","affiliation":[]},{"given":"Vanessa","family":"Moreno","sequence":"additional","affiliation":[]},{"given":"Maria","family":"Merino","sequence":"additional","affiliation":[]},{"given":"Bradford J.","family":"Wood","sequence":"additional","affiliation":[]},{"given":"Jin Tae","family":"Kwak","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"56_CR1","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1016\/S0169-7161(82)02042-2","volume":"2","author":"AK Jain","year":"1982","unstructured":"Jain, A.K., Chandrasekaran, B.: 39 Dimensionality and sample size considerations in pattern recognition practice. Handb. Stat. 2, 835\u2013855 (1982)","journal-title":"Handb. Stat."},{"key":"56_CR2","first-page":"2079","volume":"11","author":"GC Cawley","year":"2010","unstructured":"Cawley, G.C., Talbot, N.L.: On over-fitting in model selection and subsequent selection bias in performance evaluation. J. Mach. Learn. Res. 11, 2079\u20132107 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"56_CR3","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1109\/34.75512","volume":"13","author":"SJ Raudys","year":"1991","unstructured":"Raudys, S.J., Jain, A.K.: Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Trans. Pattern Anal. Mach. Intell. 13, 252\u2013264 (1991)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"56_CR4","doi-asserted-by":"crossref","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. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"56_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2200\/S00196ED1V01Y200906AIM006","volume":"3","author":"X Zhu","year":"2009","unstructured":"Zhu, X., Goldberg, A.B.: Introduction to semi-supervised learning. Synth. Lect. Artif. Intell. Mach. Learn. 3, 1\u2013130 (2009)","journal-title":"Synth. Lect. Artif. Intell. Mach. Learn."},{"key":"56_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/978-3-319-66185-8_29","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2212 MICCAI 2017","author":"W Bai","year":"2017","unstructured":"Bai, W., et al.: Semi-supervised learning for network-based cardiac MR image segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 253\u2013260. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66185-8_29"},{"key":"56_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/978-3-319-66179-7_36","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2212 MICCAI 2017","author":"C Baur","year":"2017","unstructured":"Baur, C., Albarqouni, S., Navab, N.: Semi-supervised deep learning for fully convolutional networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 311\u2013319. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_36"},{"key":"56_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1007\/978-3-030-32239-7_32","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"S Sedai","year":"2019","unstructured":"Sedai, S., et al.: Uncertainty guided semi-supervised segmentation of retinal layers in OCT images. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 282\u2013290. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_32"},{"key":"56_CR9","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1186\/1741-7015-10-100","volume":"10","author":"J Sorace","year":"2012","unstructured":"Sorace, J., Aberle, D.R., Elimam, D., Lawvere, S., Tawfik, O., Wallace, W.D.: Integrating pathology and radiology disciplines: an emerging opportunity? BMC Med. 10, 100 (2012)","journal-title":"BMC Med."},{"key":"56_CR10","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1148\/radiol.10091343","volume":"255","author":"DL Langer","year":"2010","unstructured":"Langer, D.L., et al.: Prostate tissue composition and MR measurements: investigating the relationships between ADC, T2, K trans, ve, and corresponding histologic features. Radiology 255, 485\u2013494 (2010)","journal-title":"Radiology"},{"key":"56_CR11","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1148\/radiol.2017160906","volume":"285","author":"JT Kwak","year":"2017","unstructured":"Kwak, J.T., et al.: Prostate cancer: a correlative study of multiparametric MR imaging and digital histopathology. Radiology 285, 147\u2013156 (2017)","journal-title":"Radiology"},{"key":"56_CR12","unstructured":"Xie, Q., Dai, Z., Hovy, E., Luong, M.-T., Le, Q.V.: Unsupervised data augmentation. arXiv preprint (2019)"},{"key":"56_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/978-3-319-67534-3_17","volume-title":"Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis","author":"A BenTaieb","year":"2017","unstructured":"BenTaieb, A., Hamarneh, G.: Uncertainty driven multi-loss fully convolutional networks for histopathology. In: Cardoso, M.J., et al. (eds.) LABELS\/CVII\/STENT -2017. LNCS, vol. 10552, pp. 155\u2013163. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67534-3_17"},{"key":"56_CR14","unstructured":"Smith, S.L., Kindermans, P.-J., Le, Q.V.: Don\u2019t decay the learning rate, increase the batch size. In: International Conference on Learning Representations (2018)"},{"key":"56_CR15","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"56_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"56_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59710-8_56","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:10:47Z","timestamp":1759356647000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59710-8_56"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597092","9783030597108"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59710-8_56","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","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":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2020.org\/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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1809","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":"542","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":"30% - 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":"4","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)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}