{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:20:49Z","timestamp":1742919649265,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031250811"},{"type":"electronic","value":"9783031250828"}],"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\/978-3-031-25082-8_39","type":"book-chapter","created":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T09:12:42Z","timestamp":1676106762000},"page":"577-592","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Relieving Pixel-Wise Labeling Effort for\u00a0Pathology Image Segmentation with\u00a0Self-training"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8117-8913","authenticated-orcid":false,"given":"Romain","family":"Mormont","sequence":"first","affiliation":[]},{"given":"Mehdi","family":"Testouri","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9587-1954","authenticated-orcid":false,"given":"Rapha\u00ebl","family":"Mar\u00e9e","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8527-5000","authenticated-orcid":false,"given":"Pierre","family":"Geurts","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,12]]},"reference":[{"key":"39_CR1","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","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"},{"issue":"1","key":"39_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-17204-5","volume":"7","author":"P Bankhead","year":"2017","unstructured":"Bankhead, P., et al.: QuPath: open source software for digital pathology image analysis. Sci. Rep. 7(1), 1\u20137 (2017)","journal-title":"Sci. Rep."},{"key":"39_CR3","doi-asserted-by":"publisher","unstructured":"Berg, S., et al.: ilastik: interactive machine learning for (bio)image analysis. Nat. Methods (2019). https:\/\/doi.org\/10.1038\/s41592-019-0582-9","DOI":"10.1038\/s41592-019-0582-9"},{"key":"39_CR4","unstructured":"Bokhorst, J.M., Pinckaers, H., van Zwam, P., Nagtegaal, I., van der Laak, J., Ciompi, F.: Learning from sparsely annotated data for semantic segmentation in histopathology images. In: International Conference on Medical Imaging with Deep Learning-Full Paper Track (2018)"},{"issue":"8","key":"39_CR5","doi-asserted-by":"publisher","first-page":"2626","DOI":"10.1109\/TMI.2020.2996645","volume":"39","author":"DP Fan","year":"2020","unstructured":"Fan, D.P., et al.: Inf-net: automatic Covid-19 lung infection segmentation from CT images. IEEE Trans. Med. Imaging 39(8), 2626\u20132637 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"39_CR6","unstructured":"Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. In: Advances in Neural Information Processing Systems, vol. 17 (2004)"},{"issue":"1","key":"39_CR7","first-page":"1","volume":"1","author":"A Gupta","year":"2021","unstructured":"Gupta, A., Gupta, R., Gehlot, S., Goswami, S.: SegPC-2021: segmentation of multiple myeloma plasma cells in microscopic images. IEEE Dataport 1(1), 1 (2021)","journal-title":"IEEE Dataport"},{"key":"39_CR8","unstructured":"Jaiswal, A.K., Panshin, I., Shulkin, D., Aneja, N., Abramov, S.: Semi-supervised learning for cancer detection of lymph node metastases. arXiv preprint arXiv:1906.09587 (2019)"},{"key":"39_CR9","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"39_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101771","volume":"65","author":"NA Koohbanani","year":"2020","unstructured":"Koohbanani, N.A., Jahanifar, M., Tajadin, N.Z., Rajpoot, N.: NuClick: a deep learning framework for interactive segmentation of microscopic images. Med. Image Anal. 65, 101771 (2020)","journal-title":"Med. Image Anal."},{"issue":"10","key":"39_CR11","doi-asserted-by":"publisher","first-page":"2845","DOI":"10.1109\/TMI.2021.3056023","volume":"40","author":"NA Koohbanani","year":"2021","unstructured":"Koohbanani, N.A., Unnikrishnan, B., Khurram, S.A., Krishnaswamy, P., Rajpoot, N.: Self-path: self-supervision for classification of pathology images with limited annotations. IEEE Trans. Med. Imaging 40(10), 2845\u20132856 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"39_CR12","doi-asserted-by":"publisher","first-page":"1380","DOI":"10.1109\/TMI.2019.2947628","volume":"39","author":"N Kumar","year":"2019","unstructured":"Kumar, N., et al.: A multi-organ nucleus segmentation challenge. IEEE Trans. Med. Imaging 39(5), 1380\u20131391 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"39_CR13","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1145\/882262.882264","volume":"22","author":"V Kwatra","year":"2003","unstructured":"Kwatra, V., Sch\u00f6dl, A., Essa, I., Turk, G., Bobick, A.: Graphcut textures: image and video synthesis using graph cuts. ACM Trans. Graph. (TOG) 22(3), 277\u2013286 (2003)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"39_CR14","unstructured":"Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. arXiv preprint arXiv:1610.02242 (2016)"},{"key":"39_CR15","unstructured":"Lee, D.H., et al.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on challenges in representation learning, ICML, vol. 3, p. 896 (2013)"},{"key":"39_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"842","DOI":"10.1007\/978-3-030-20351-1_66","volume-title":"Information Processing in Medical Imaging","author":"J Li","year":"2019","unstructured":"Li, J., et al.: Signet ring cell detection with a semi-supervised learning framework. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 842\u2013854. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20351-1_66"},{"key":"39_CR17","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.compmedimag.2018.08.003","volume":"69","author":"J Li","year":"2018","unstructured":"Li, J., et al.: An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies. Comput. Med. Imaging Graph. 69, 125\u2013133 (2018)","journal-title":"Comput. Med. Imaging Graph."},{"key":"39_CR18","doi-asserted-by":"crossref","unstructured":"Maninis, K.K., Caelles, S., Pont-Tuset, J., Van Gool, L.: Deep extreme cut: from extreme points to object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 616\u2013625 (2018)","DOI":"10.1109\/CVPR.2018.00071"},{"issue":"9","key":"39_CR19","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1093\/bioinformatics\/btw013","volume":"32","author":"R Mar\u00e9e","year":"2016","unstructured":"Mar\u00e9e, R., et al.: Collaborative analysis of multi-gigapixel imaging data using Cytomine. Bioinformatics 32(9), 1395\u20131401 (2016)","journal-title":"Bioinformatics"},{"issue":"1","key":"39_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-24876-0","volume":"8","author":"M Peikari","year":"2018","unstructured":"Peikari, M., Salama, S., Nofech-Mozes, S., Martel, A.L.: A cluster-then-label semi-supervised learning approach for pathology image classification. Sci. Rep. 8(1), 1\u201313 (2018)","journal-title":"Sci. Rep."},{"key":"39_CR21","doi-asserted-by":"crossref","unstructured":"Peng, J., Wang, Y.: Medical image segmentation with limited supervision: a review of deep network models. IEEE Access (2021)","DOI":"10.1109\/ACCESS.2021.3062380"},{"key":"39_CR22","doi-asserted-by":"crossref","unstructured":"Pham, H., Dai, Z., Xie, Q., Le, Q.V.: Meta pseudo labels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11557\u201311568 (2021)","DOI":"10.1109\/CVPR46437.2021.01139"},{"key":"39_CR23","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":"39_CR24","doi-asserted-by":"crossref","unstructured":"Rother, C., Kolmogorov, V., Blake, A.: \u201cGrabCut\u201d interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. (TOG) 23(3), 309\u2013314 (2004)","DOI":"10.1145\/1015706.1015720"},{"key":"39_CR25","unstructured":"Shaw, S., Pajak, M., Lisowska, A., Tsaftaris, S.A., O\u2019Neil, A.Q.: Teacher-student chain for efficient semi-supervised histology image classification. arXiv preprint arXiv:2003.08797 (2020)"},{"key":"39_CR26","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.media.2016.08.008","volume":"35","author":"K Sirinukunwattana","year":"2017","unstructured":"Sirinukunwattana, K., et al.: Gland segmentation in colon histology images: the GlaS challenge contest. Med. Image Anal. 35, 489\u2013502 (2017)","journal-title":"Med. Image Anal."},{"key":"39_CR27","unstructured":"Sohn, K., et al.: FixMatch: simplifying semi-supervised learning with consistency and confidence. In: Advances in Neural Information Processing Systems, vol. 33, pp. 596\u2013608 (2020)"},{"key":"39_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1007\/978-3-030-32239-7_62","volume-title":"Medical Image Computing and Computer Assisted Intervention","author":"H Su","year":"2019","unstructured":"Su, H., Shi, X., Cai, J., Yang, L.: Local and global consistency regularized mean teacher for semi-supervised nuclei classification. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 559\u2013567. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_62"},{"key":"39_CR29","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.N., 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":"39_CR30","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)"},{"issue":"9","key":"39_CR31","doi-asserted-by":"publisher","first-page":"2126","DOI":"10.1109\/TMI.2018.2820199","volume":"37","author":"D Tellez","year":"2018","unstructured":"Tellez, D., et al.: Whole-slide mitosis detection in H &E breast histology using PHH3 as a reference to train distilled stain-invariant convolutional networks. IEEE Trans. Med. Imaging 37(9), 2126\u20132136 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"39_CR32","doi-asserted-by":"crossref","unstructured":"Xie, Q., Luong, M.T., Hovy, E., Le, Q.V.: Self-training with noisy student improves imagenet classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10687\u201310698 (2020)","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"39_CR33","doi-asserted-by":"crossref","unstructured":"Yarowsky, D.: Unsupervised word sense disambiguation rivaling supervised methods. In: 33rd Annual Meeting of the Association for Computational Linguistics, pp. 189\u2013196 (1995)","DOI":"10.3115\/981658.981684"},{"key":"39_CR34","unstructured":"Zhu, Y., et al.: Improving semantic segmentation via self-training. arXiv preprint arXiv:2004.14960 (2020)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25082-8_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T13:11:01Z","timestamp":1709817061000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25082-8_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250811","9783031250828"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25082-8_39","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"12 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","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":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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":"From the workshops, 367 reviewed full papers have been selected for publication","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)"}}]}}