{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T22:20:05Z","timestamp":1781734805187,"version":"3.54.5"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031471964","type":"print"},{"value":"9783031449178","type":"electronic"}],"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\/978-3-031-44917-8_21","type":"book-chapter","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T20:33:48Z","timestamp":1696710828000},"page":"213-223","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Test-Time Augmentation-Based Active Learning and\u00a0Self-training for\u00a0Label-Efficient Segmentation"],"prefix":"10.1007","author":[{"given":"Bella","family":"Specktor-Fadida","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Levchakov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dana","family":"Schonberger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liat","family":"Ben-Sira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dafna","family":"Ben Bashat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leo","family":"Joskowicz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,8]]},"reference":[{"key":"21_CR1","doi-asserted-by":"crossref","unstructured":"Arazo, E., Ortego, D., Albert, P., O\u2019Connor, N., McGuinness, K.: Pseudo-labeling and confirmation bias in deep semi-supervised learning. In: Proceedings of International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138 (2020)","DOI":"10.1109\/IJCNN48605.2020.9207304"},{"key":"21_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102062","volume":"71","author":"S Budd","year":"2021","unstructured":"Budd, S., Robinson, E., Kainz, B.: A survey on active learning and human-in-the-loop deep learning for medical image analysis. Med. Image Anal. 71, 102062 (2021)","journal-title":"Med. Image Anal."},{"key":"21_CR3","doi-asserted-by":"crossref","unstructured":"Chen, X., Yuan, Y., Zeng, G. & Wang, J. Semi-supervised semantic segmentation with cross pseudo supervision. In: Proceedings of IEEE\/CVF Conference On Computer Vision and Pattern Recognition, pp. 2613\u20132622 (2021)","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"21_CR4","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.media.2019.03.009","volume":"54","author":"V Cheplygina","year":"2019","unstructured":"Cheplygina, V., Bruijne, M., Pluim, J.: Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. Med. Image Anal. 54, 280\u2013296 (2019)","journal-title":"Med. Image Anal."},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"Dudovitch, G., Link-Sourani, D., Ben Sira, L., Miller, E., Ben Bashat, D., Joskowicz, L.: Deep learning automatic fetal structures segmentation in MRI scans with few annotated datasets. Medical Image Computing And Computer Assisted Intervention-MICCAI 2020: 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part VI 23, pp. 365\u2013374 (2020)","DOI":"10.1007\/978-3-030-59725-2_35"},{"key":"21_CR6","doi-asserted-by":"publisher","DOI":"10.5244\/C.25.78","volume-title":"Combining self training and active learning for video segmentation","author":"A Fathi","year":"2011","unstructured":"Fathi, A., Balcan, M., Ren, X., Rehg, J.: Combining self training and active learning for video segmentation. Technical report, Georgia Institute of Technology (2011)"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Gaillochet, M., Desrosiers, C., Lombaert, H.: TAAL: test-time augmentation for active learning in medical image segmentation. Data Augmentation, Labelling, And Imperfections: Second MICCAI Workshop, DALI 2022, Held In Conjunction With MICCAI 2022, Singapore, September 22, 2022, Proceedings. pp. 43\u201353 (2022)","DOI":"10.1007\/978-3-031-17027-0_5"},{"key":"21_CR8","unstructured":"Guan, L., Yuan, X.: Iterative Loop Learning Combining Self-Training and Active Learning for Domain Adaptive Semantic Segmentation. ArXiv Preprint ArXiv:2301.13361 (2023)"},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Jungo, A., Reyes, M.: Assessing reliability and challenges of uncertainty estimations for medical image segmentation. In: Medical Image Computing And Computer Assisted Intervention-MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part II 22, pp. 48\u201356 (2019)","DOI":"10.1007\/978-3-030-32245-8_6"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Lai, Z., Wang, C., Oliveira, L., Dugger, B., Cheung, S., Chuah, C.: Joint semi-supervised and active learning for segmentation of gigapixel pathology images with cost-effective labeling. In: Proceedings IEEE\/CVF International Conference On Computer Vision, pp. 591\u2013600 (2021)","DOI":"10.1109\/ICCVW54120.2021.00072"},{"key":"21_CR11","unstructured":"Loshchilov, I., Hutter, F.: Sgdr: stochastic gradient descent with warm restarts. ArXiv Preprint ArXiv:1608.03983 (2016)"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Nath, V., Yang, D., Roth, H., Xu, D.: Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: Medical Image Computing And Computer Assisted Intervention-MICCAI 2022: 25th International Conference, Singapore, September 18\u201322, 2022, Proceedings, Part VIII, pp. 297\u2013308 (2022)","DOI":"10.1007\/978-3-031-16452-1_29"},{"key":"21_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1007\/978-3-030-00937-3_43","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"D Nie","year":"2018","unstructured":"Nie, D., Gao, Y., Wang, L., Shen, D.: ASDNet: attention based semi-supervised deep networks for medical image segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 370\u2013378. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_43"},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Ouali, Y., Hudelot, C., Tami, M.: Semi-supervised semantic segmentation with cross-consistency training. In: Proceedings Of The IEEE\/CVF Conference On Computer Vision and Pattern Recognition, pp. 12674\u201312684 (2020)","DOI":"10.1109\/CVPR42600.2020.01269"},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Shen, W., et al.: A survey on label-efficient deep image segmentation: bridging the gap between weak supervision and dense prediction. IEEE Trans. Pattern Anal. Mach. Intell. (2023)","DOI":"10.1109\/TPAMI.2023.3246102"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Specktor-Fadida, B., et al.: A bootstrap self-training method for sequence transfer: state-of-the-art placenta segmentation in fetal MRI. Uncertainty For Safe Utilization Of Machine Learning In Medical Imaging, And Perinatal Imaging, Placental And Preterm Image Analysis: 3rd International Workshop, UNSURE 2021, And 6th International Workshop, PIPPI 2021, Held In Conjunction With MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings 3. pp. 189\u2013199 (2021)","DOI":"10.1007\/978-3-030-87735-4_18"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Specktor-Fadida, B., Link-Sourani, D., Ben-Sira, L., Miller, E., Ben-Bashat, D., Joskowicz, L.: Partial annotations for the segmentation of large structures with low annotation cost. Medical Image Learning With Limited And Noisy Data: First International Workshop, MILLanD 2022, Held In Conjunction With MICCAI 2022, Singapore, September 22, 2022, Proceedings. pp. 13\u201322 (2022)","DOI":"10.1007\/978-3-031-16760-7_2"},{"key":"21_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101693","volume":"63","author":"N Tajbakhsh","year":"2020","unstructured":"Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J., Wu, Z., Ding, X.: Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation. Med. Image Anal. 63, 101693 (2020)","journal-title":"Med. Image Anal."},{"key":"21_CR19","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2019.01.103","volume":"338","author":"G Wang","year":"2019","unstructured":"Wang, G., Li, W., Aertsen, M., Deprest, J., Ourselin, S., Vercauteren, T.: Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks. Neurocomputing 338, 34\u201345 (2019)","journal-title":"Neurocomputing"},{"key":"21_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101766","volume":"65","author":"Y Xia","year":"2020","unstructured":"Xia, Y., Yang, D., Yu, Z., Liu, F., Cai, J., Yu, L., Zhu, Z., Xu, D., Yuille, A., Roth, H.: Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation. Med. Image Anal. 65, 101766 (2020)","journal-title":"Med. Image Anal."},{"key":"21_CR21","doi-asserted-by":"crossref","unstructured":"Yang, L., Zhuo, W., Qi, L., Shi, Y., Gao, Y.: St++: make self-training work better for semi-supervised semantic segmentation. Proceedings of the IEEE\/CVF Conference On Computer Vision And Pattern Recognition, pp. 4268\u20134277 (2022)","DOI":"10.1109\/CVPR52688.2022.00423"},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"Yang, L., Zhang, Y., Chen, J., Zhang, S., Chen, D.: Suggestive annotation: a deep active learning framework for biomedical image segmentation. Medical Image Computing And Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11\u201313, 2017, Proceedings, Part III 20, pp. 399\u2013407 (2017)","DOI":"10.1007\/978-3-319-66179-7_46"},{"key":"21_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, W., Zhu, L., Hallinan, J., Zhang, S., Makmur, A., Cai, Q., Ooi, B.: Boostmis: Boosting medical image semi-supervised learning with adaptive pseudo labeling and informative active annotation. Proceegings of IEEE\/CVF Conference On Computer Vision and Pattern Recognition, pp. 20666\u201320676 (2022)","DOI":"10.1109\/CVPR52688.2022.02001"},{"key":"21_CR24","doi-asserted-by":"crossref","unstructured":"Zhu, Y., et al.: Improving semantic segmentation via efficient self-training. IEEE Trans. Pattern Anal. Mach. Intell. (2021)","DOI":"10.1109\/TPAMI.2021.3138337"},{"key":"21_CR25","first-page":"3833","volume":"33","author":"B Zoph","year":"2020","unstructured":"Zoph, B., Ghiasi, G., Lin, T., Cui, Y., Liu, H., Cubuk, E., Le, Q.: Rethinking pre-training and self-training. Adv. Neural. Inf. Process. Syst. 33, 3833\u20133845 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."}],"container-title":["Lecture Notes in Computer Science","Medical Image Learning with Limited and Noisy Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44917-8_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T20:37:54Z","timestamp":1696711074000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44917-8_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031471964","9783031449178"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44917-8_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"8 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MILLanD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Workshop on Medical Image Learning with Limited and Noisy Data","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":"8 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"milland2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccaimilland.wixsite.com\/milland2023","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":"38","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":"25","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":"66% - 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":"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)"}}]}}