{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T09:15:04Z","timestamp":1743066904530,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031210136"},{"type":"electronic","value":"9783031210143"}],"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\/978-3-031-21014-3_15","type":"book-chapter","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T13:43:40Z","timestamp":1671111820000},"page":"140-150","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Cross Task Temporal Consistency for\u00a0Semi-supervised Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Govind","family":"Jeevan","sequence":"first","affiliation":[]},{"given":"S. J.","family":"Pawan","sequence":"additional","affiliation":[]},{"given":"Jeny","family":"Rajan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"15_CR1","unstructured":"Lee, D.-H.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: ICML, pp. 03\u2013896 (2013)"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Li, R., Auer, D., Wagner, C., Chen, X.: A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation. In: ISBI, pp. 1168\u20131172 (2020)","DOI":"10.1109\/ISBI45749.2020.9098568"},{"key":"15_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1007\/978-3-030-59710-8_60","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"Y Li","year":"2020","unstructured":"Li, Y., Chen, J., Xie, X., Ma, K., Zheng, Y.: Self-loop uncertainty: a novel pseudo-label for semi-supervised medical image segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 614\u2013623. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_60"},{"key":"15_CR4","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":"15_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1007\/978-3-319-66179-7_47","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"Y Zhang","year":"2017","unstructured":"Zhang, Y., Yang, L., Chen, J., Fredericksen, M., Hughes, D.P., Chen, D.Z.: Deep adversarial networks for biomedical image segmentation utilizing unannotated images. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 408\u2013416. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_47"},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Souly, N., Spampinato, C., Shah, M.: Semi supervised semantic segmentation using generative adversarial network. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5688\u20135696 (2017)","DOI":"10.1109\/ICCV.2017.606"},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"Ma, Y., et al.: Self-supervised vessel segmentation via adversarial learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7536\u20137545 (2021)","DOI":"10.1109\/ICCV48922.2021.00744"},{"key":"15_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1007\/978-3-030-32245-8_67","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"L Yu","year":"2019","unstructured":"Yu, L., Wang, S., Li, X., Fu, C.-W., Heng, P.-A.: Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 605\u2013613. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_67"},{"key":"15_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1007\/978-3-030-59710-8_54","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"S Li","year":"2020","unstructured":"Li, S., Zhang, C., He, X.: Shape-aware semi-supervised 3D semantic segmentation for medical images. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 552\u2013561. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_54"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Ouali, Y., Hudelot, C., Tami, M.: Semi-supervised semantic segmentation with cross-consistency training. In: IEEE-CVF, pp. 12674\u201312684 (2020)","DOI":"10.1109\/CVPR42600.2020.01269"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Luo, X., Chen, J., Song, T., Wang, G.: Semi-supervised medical image segmentation through dual-task consistency. In: AAAI Conference on Artificial Intelligence (2021)","DOI":"10.1609\/aaai.v35i10.17066"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Chen, X., Yuan, Y., Zeng, G., Wang, J.: Semi-supervised semantic segmentation with cross pseudo supervision. In: IEEE-CVF, pp. 2613\u20132622 (2021)","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"15_CR13","first-page":"108021","volume":"239","author":"H Lin","year":"2021","unstructured":"Lin, H., et al.: Semi-supervised NPC segmentation with uncertainty and attention guided consistency. Knowl.-Based Syst. 239, 108021 (2021)","journal-title":"Knowl.-Based Syst."},{"key":"15_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1007\/978-3-030-87196-3_30","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"X Luo","year":"2021","unstructured":"Luo, X., et al.: Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 318\u2013329. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87196-3_30"},{"key":"15_CR15","doi-asserted-by":"publisher","first-page":"102073","DOI":"10.1016\/j.artmed.2021.102073","volume":"116","author":"M Anneke","year":"2021","unstructured":"Anneke, M., et al.: Uncertainty-aware temporal self-learning (UATS): semi-supervised learning for segmentation of prostate zones and beyond. Artif. Intell. Med. 116, 102073 (2021)","journal-title":"Artif. Intell. Med."},{"key":"15_CR16","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"15_CR17","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":"15_CR18","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.-A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: IEEE 2016 Fourth International Conference on 3D Vision, pp. 565\u2013571 (2016)","DOI":"10.1109\/3DV.2016.79"},{"issue":"3","key":"15_CR19","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"LR Dice","year":"1945","unstructured":"Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297\u2013302 (1945)","journal-title":"Ecology"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Xue, Y., et al.: Shape-aware organ segmentation by predicting signed distance maps. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 07, pp. 12565\u201312572 (2020)","DOI":"10.1609\/aaai.v34i07.6946"},{"key":"15_CR21","unstructured":"French, G., Mackiewicz, M., Fisher, M.H.: Self-ensembling for visual domain adaptation. In: International Conference on Learning Representations (2018)"},{"key":"15_CR22","unstructured":"Zhou, T., Wang, S., Bilmes, J.: Time-consistent self-supervision for semi-supervised learning. In: International Conference on Machine Learning, pp. 11523\u201311533 (2020)"},{"key":"15_CR23","first-page":"101832","volume":"34","author":"X Zhaohan","year":"2021","unstructured":"Zhaohan, X., et al.: A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging. Med. Image Anal. 34, 101832 (2021)","journal-title":"Med. Image Anal."},{"key":"15_CR24","doi-asserted-by":"crossref","unstructured":"Bernard, O., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?. IEEE Trans. Med. Imaging 37(11), 2514\u20132525 (2018). https:\/\/www.creatis.insa-lyon.fr\/Challenge\/acdc\/index.html","DOI":"10.1109\/TMI.2018.2837502"},{"key":"15_CR25","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 \u2013 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":"15_CR26","unstructured":"Luo, X.: SSL4MIS (2020). https:\/\/github.com\/HiLab-git\/SSL4MIS"},{"key":"15_CR27","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.neunet.2021.10.008","volume":"145","author":"V Verma","year":"2022","unstructured":"Verma, V., et al.: Interpolation consistency training for semi-supervised learning. Neural Netw. 145, 90\u2013106 (2022)","journal-title":"Neural Netw."},{"key":"15_CR28","doi-asserted-by":"crossref","unstructured":"Vu, T., et al.: ADVENT: adversarial entropy minimization for domain adaptation in semantic segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2512\u20132521 (2019)","DOI":"10.1109\/CVPR.2019.00262"},{"key":"15_CR29","doi-asserted-by":"publisher","first-page":"107269","DOI":"10.1016\/j.patcog.2020.107269","volume":"107","author":"J Peng","year":"2020","unstructured":"Peng, J., et al.: Deep co-training for semi-supervised image segmentation. Pattern Recogn. 107, 107269 (2020). ISSN 0031-3203","journal-title":"Pattern Recogn."},{"key":"15_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1007\/978-3-030-59710-8_54","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"S Li","year":"2020","unstructured":"Li, S., Zhang, C., He, X.: Shape-aware semi-supervised 3D semantic segmentation for medical images. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 552\u2013561. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_54"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21014-3_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T13:46:37Z","timestamp":1671111997000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21014-3_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031210136","9783031210143"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21014-3_15","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":"16 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","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":"18 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2022\/","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":"64","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":"48","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":"75% - 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":"2","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":"3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}