{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:03:56Z","timestamp":1775066636995,"version":"3.50.1"},"publisher-location":"Cham","reference-count":10,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030322502","type":"print"},{"value":"9783030322519","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-32251-9_42","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:08:49Z","timestamp":1570662529000},"page":"384-393","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":217,"title":["Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis"],"prefix":"10.1007","author":[{"given":"Zongwei","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Vatsal","family":"Sodha","sequence":"additional","affiliation":[]},{"given":"Md Mahfuzur","family":"Rahman Siddiquee","sequence":"additional","affiliation":[]},{"given":"Ruibin","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Nima","family":"Tajbakhsh","sequence":"additional","affiliation":[]},{"given":"Michael B.","family":"Gotway","sequence":"additional","affiliation":[]},{"given":"Jianming","family":"Liang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"key":"42_CR1","doi-asserted-by":"crossref","unstructured":"Deng, J., et al.: ImageNet: A large-scale hierarchical image database. In: CVPR, 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"42_CR2","doi-asserted-by":"crossref","unstructured":"Doersch, C., et al.: Multi-task self-supervised visual learning. In: ICCV 2051\u20132060, (2017)","DOI":"10.1109\/ICCV.2017.226"},{"key":"42_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1007\/978-3-319-67558-9_34","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"A Jamaludin","year":"2017","unstructured":"Jamaludin, A., Kadir, T., Zisserman, A.: Self-supervised learning for spinal MRIs. In: Cardoso, M.J., et al. (eds.) DLMIA\/ML-CDS -2017. LNCS, vol. 10553, pp. 294\u2013302. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67558-9_34"},{"key":"42_CR4","unstructured":"Jing, L., et al.: Self-supervised visual feature learning with deep neural networks: A survey. arXiv:1902.06162 (2019)"},{"key":"42_CR5","unstructured":"Kang, G., et al.: Patchshuffle regularization. arXiv:1707.07103 (2017)"},{"key":"42_CR6","doi-asserted-by":"crossref","unstructured":"Pathak, D., et al.: Context encoders: Feature learning by inpainting. In: CVPR, 2536\u20132544 (2016)","DOI":"10.1109\/CVPR.2016.278"},{"issue":"5","key":"42_CR7","first-page":"1285","volume":"35","author":"HC Shin","year":"2016","unstructured":"Shin, H.C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. TMI 35(5), 1285\u20131298 (2016)","journal-title":"TMI"},{"key":"42_CR8","doi-asserted-by":"crossref","unstructured":"Spitzer, H., et al.: Improving cytoarchitectonic segmentation of human brain areas with self-supervised siamese networks. In: MICCAI, 663\u2013671 (2018)","DOI":"10.1007\/978-3-030-00931-1_76"},{"issue":"5","key":"42_CR9","first-page":"1299","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: Full training or fine tuning? TMI 35(5), 1299\u20131312 (2016)","journal-title":"TMI"},{"key":"42_CR10","doi-asserted-by":"crossref","unstructured":"Tajbakhsh, N., et al.: Surrogate supervision for medical image analysis: Effective deep learning from limited quantities of labeled data. In: ISBI, 1251\u20131255 (2019)","DOI":"10.1109\/ISBI.2019.8759553"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32251-9_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:17:46Z","timestamp":1728519466000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32251-9_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322502","9783030322519"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32251-9_42","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"10 October 2019","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":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2019.org\/","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":"1730","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":"539","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":"3.07","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":"6.31","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":"This content has been made available to all.","name":"free","label":"Free to read"}]}}