{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:16:59Z","timestamp":1742912219041,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031239106"},{"type":"electronic","value":"9783031239113"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-23911-3_14","type":"book-chapter","created":{"date-parts":[[2023,1,21]],"date-time":"2023-01-21T01:06:10Z","timestamp":1674263170000},"page":"152-162","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Teacher-Student Semi-supervised Approach for\u00a0Medical Image Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9379-8151","authenticated-orcid":false,"given":"Maria","family":"Baldeon Calisto","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,21]]},"reference":[{"key":"14_CR1","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.neunet.2020.03.007","volume":"126","author":"M Baldeon Calisto","year":"2020","unstructured":"Baldeon Calisto, M., Lai-Yuen, S.K.: AdaEn-Net\u202f: an ensemble of adaptive 2D\u20133D fully convolutional networks for medical image segmentation. Neural Netw. 126, 76\u201394 (2020). https:\/\/doi.org\/10.1016\/j.neunet.2020.03.007","journal-title":"Neural Netw."},{"key":"14_CR2","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.neucom.2019.01.110","volume":"392","author":"M Baldeon-Calisto","year":"2020","unstructured":"Baldeon-Calisto, M., Lai-Yuen, S.K.: AdaResU-Net: Multiobjective adaptive convolutional neural network for medical image segmentation. Neurocomputing 392, 325\u2013340 (2020). https:\/\/doi.org\/10.1016\/j.neucom.2019.01.110","journal-title":"Neurocomputing"},{"key":"14_CR3","doi-asserted-by":"publisher","unstructured":"Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2) (2020). https:\/\/doi.org\/10.3390\/info11020125, https:\/\/www.mdpi.com\/2078-2489\/11\/2\/125","DOI":"10.3390\/info11020125"},{"key":"14_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/978-3-030-32248-9_51","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"S Chen","year":"2019","unstructured":"Chen, S., Bortsova, G., Garc\u00eda-Uceda Ju\u00e1rez, A., van Tulder, G., de Bruijne, M.: Multi-task attention-based semi-supervised learning for medical image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 457\u2013465. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_51"},{"issue":"6","key":"14_CR5","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045\u20131057 (2013)","journal-title":"J. Digit. Imaging"},{"key":"14_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101821","volume":"67","author":"N Heller","year":"2021","unstructured":"Heller, N., et al.: The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: results of the kits19 challenge. Med. Image Anal. 67, 101821 (2021)","journal-title":"Med. Image Anal."},{"issue":"6","key":"14_CR7","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1200\/JCO.2020.38.6_suppl.626","volume":"38","author":"N Heller","year":"2020","unstructured":"Heller, N., et al.: An international challenge to use artificial intelligence to define the state-of-the-art in kidney and kidney tumor segmentation in CT imaging. Proc. Am. Soc. Clin. Oncol. 38(6), 626\u2013626 (2020)","journal-title":"Proc. Am. Soc. Clin. Oncol."},{"key":"14_CR8","unstructured":"Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets. In: Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, PMLR 38 2015. pp. 562\u2013570 (2015)"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Luo, X., Chen, J., Song, T., Wang, G.: Semi-supervised medical image segmentation through dual-task consistency. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 8801\u20138809 (2021)","DOI":"10.1609\/aaai.v35i10.17066"},{"key":"14_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102035","volume":"71","author":"J Ma","year":"2021","unstructured":"Ma, J., et al.: Loss odyssey in medical image segmentation. Med. Image Anal. 71, 102035 (2021)","journal-title":"Med. Image Anal."},{"key":"14_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102616","volume":"82","author":"J Ma","year":"2022","unstructured":"Ma, J., et al.: Fast and low-GPU-memory abdomen CT organ segmentation: the flare challenge. Med. Image Anal. 82, 102616 (2022). https:\/\/doi.org\/10.1016\/j.media.2022.102616","journal-title":"Med. Image Anal."},{"issue":"10","key":"14_CR12","doi-asserted-by":"crossref","first-page":"6695","DOI":"10.1109\/TPAMI.2021.3100536","volume":"44","author":"J Ma","year":"2022","unstructured":"Ma, J., et al.: AbdomenCT-1K: Is abdominal organ segmentation a solved problem? IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6695\u20136714 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"14_CR14","unstructured":"Simpson, A.L., et al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063 (2019)"},{"issue":"10","key":"14_CR15","doi-asserted-by":"publisher","first-page":"2926","DOI":"10.1109\/TMI.2021.3059265","volume":"40","author":"D Tomar","year":"2021","unstructured":"Tomar, D., Lortkipanidze, M., Vray, G., Bozorgtabar, B., Thiran, J.P.: Self-attentive spatial adaptive normalization for cross-modality domain adaptation. IEEE Trans. Med. Imaging 40(10), 2926\u20132938 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"14_CR16","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","volume":"109","author":"JE Van Engelen","year":"2020","unstructured":"Van Engelen, J.E., Hoos, H.H.: A survey on semi-supervised learning. Mach. Learn. 109(2), 373\u2013440 (2020)","journal-title":"Mach. Learn."}],"container-title":["Lecture Notes in Computer Science","Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-23911-3_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,5]],"date-time":"2023-12-05T04:25:26Z","timestamp":1701750326000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-23911-3_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031239106","9783031239113"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-23911-3_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"21 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FLARE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation","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":"22 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":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"flare2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/flare22.grand-challenge.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"openreview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"48","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":"28","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":"58% - 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":"4","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)"}}]}}