{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T16:03:37Z","timestamp":1776182617733,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031250811","type":"print"},{"value":"9783031250828","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\/978-3-031-25082-8_27","type":"book-chapter","created":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T09:12:42Z","timestamp":1676106762000},"page":"408-423","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automatic Grading of\u00a0Cervical Biopsies by\u00a0Combining Full and\u00a0Self-supervision"],"prefix":"10.1007","author":[{"given":"M\u00e9lanie","family":"Lubrano","sequence":"first","affiliation":[]},{"given":"Tristan","family":"Lazard","sequence":"additional","affiliation":[]},{"given":"Guillaume","family":"Balezo","sequence":"additional","affiliation":[]},{"given":"Ya\u00eblle","family":"Bellahsen-Harrar","sequence":"additional","affiliation":[]},{"given":"C\u00e9cile","family":"Badoual","sequence":"additional","affiliation":[]},{"given":"Sylvain","family":"Berlemont","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Walter","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,12]]},"reference":[{"issue":"11","key":"27_CR1","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1038\/s41571-019-0252-y","volume":"16","author":"K Bera","year":"2019","unstructured":"Bera, K., Schalper, K.A., Rimm, D.L., Velcheti, V., Madabhushi, A.: Artificial intelligence in digital pathology-new tools for diagnosis and precision oncology. Nat. Rev. Clin. Oncol. 16(11), 703\u2013715 (2019)","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"27_CR2","doi-asserted-by":"publisher","unstructured":"Campanella, G., et al.: Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25(8), 1301\u20131309 (2019). https:\/\/doi.org\/10.1038\/s41591-019-0508-1. https:\/\/www.nature.com\/articles\/s41591-019-0508-1","DOI":"10.1038\/s41591-019-0508-1"},{"key":"27_CR3","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations (2020). https:\/\/arxiv.org\/abs\/2002.05709v3"},{"key":"27_CR4","unstructured":"Chung, Y.A., Lin, H.T., Yang, S.W.: Cost-aware pre-training for multiclass cost-sensitive deep learning. IJCAI (2016)"},{"key":"27_CR5","doi-asserted-by":"publisher","unstructured":"Coudray, N., et al.: Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24(10), 1559\u20131567 (2018). https:\/\/doi.org\/10.1038\/s41591-018-0177-5. https:\/\/www.nature.com\/articles\/s41591-018-0177-5","DOI":"10.1038\/s41591-018-0177-5"},{"key":"27_CR6","unstructured":"Courtiol, P., Tramel, E.W., Sanselme, M., Wainrib, G.: Classification and disease localization in histopathology using only global labels: a weakly-supervised approach. arXiv:1802.02212 [cs, stat] (2020)"},{"key":"27_CR7","unstructured":"Dehaene, O., Camara, A., Moindrot, O., de Lavergne, A., Courtiol, P.: Self-supervision closes the gap between weak and strong supervision in histology (2020). https:\/\/arxiv.org\/abs\/2012.03583v1"},{"key":"27_CR8","doi-asserted-by":"publisher","unstructured":"Diao, J.A., et al.: Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes. Nat. Commun. 12(1), 1613 (2021). https:\/\/doi.org\/10.1038\/s41467-021-21896-9. https:\/\/www.nature.com\/articles\/s41467-021-21896-9","DOI":"10.1038\/s41467-021-21896-9"},{"key":"27_CR9","unstructured":"DrivenData: TissueNet: Detect Lesions in Cervical Biopsies. https:\/\/www.drivendata.org\/competitions\/67\/competition-cervical-biopsy\/page\/254\/"},{"key":"27_CR10","doi-asserted-by":"publisher","unstructured":"Ehteshami Bejnordi, B., et al.: Consortium: diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22), 2199\u20132210 (2017). https:\/\/doi.org\/10.1001\/jama.2017.14585","DOI":"10.1001\/jama.2017.14585"},{"key":"27_CR11","unstructured":"Erhan, D., Bengio, Y., Courville, A., Vincent, P.: Visualizing higher-layer features of a deep network. Technical report, Univerist\u00e9 de Montr\u00e9al (2009)"},{"key":"27_CR12","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. Technical report (2016). https:\/\/ui.adsabs.harvard.edu\/abs\/2016arXiv160806993H","DOI":"10.1109\/CVPR.2017.243"},{"issue":"12","key":"27_CR13","doi-asserted-by":"publisher","first-page":"3843","DOI":"10.1109\/TMI.2020.3006138","volume":"39","author":"YJ Huang","year":"2020","unstructured":"Huang, Y.J., et al.: Rectifying supporting regions with mixed and active supervision for rib fracture recognition. IEEE Trans. Med. Imaging 39(12), 3843\u20133854 (2020). https:\/\/doi.org\/10.1109\/TMI.2020.3006138","journal-title":"IEEE Trans. Med. Imaging"},{"key":"27_CR14","unstructured":"Ilse, M., Tomczak, J.M., Welling, M.: Attention-based deep multiple instance learning (2018). https:\/\/arxiv.org\/abs\/1802.04712v4"},{"key":"27_CR15","doi-asserted-by":"publisher","unstructured":"Kather, J.N., et al.: Pan-cancer image-based detection of clinically actionable genetic alterations. Nat. Cancer 1(8), 789\u2013799 (2020). https:\/\/doi.org\/10.1038\/s43018-020-0087-6. https:\/\/www.nature.com\/articles\/s43018-020-0087-6","DOI":"10.1038\/s43018-020-0087-6"},{"issue":"12","key":"27_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2022.100872","volume":"3","author":"T Lazard","year":"2022","unstructured":"Lazard, T., et al.: Deep learning identifies morphological patterns of homologous recombination deficiency in luminal breast cancers from whole slide images. Cell Rep. Med. 3(12), 100872 (2022). https:\/\/doi.org\/10.1016\/j.xcrm.2022.100872","journal-title":"Cell Rep. Med."},{"key":"27_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/978-3-030-87237-3_30","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"J Li","year":"2021","unstructured":"Li, J., et al.: Hybrid supervision learning for pathology whole slide image classification. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 309\u2013318. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87237-3_30"},{"key":"27_CR18","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: Thoracic disease identification and localization with limited supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00865"},{"key":"27_CR19","doi-asserted-by":"publisher","unstructured":"Lu, M.Y., Williamson, D.F.K., Chen, T.Y., Chen, R.J., Barbieri, M., Mahmood, F.: Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5(6), 555\u2013570 (2021). https:\/\/doi.org\/10.1038\/s41551-020-00682-w. https:\/\/www.nature.com\/articles\/s41551-020-00682-w","DOI":"10.1038\/s41551-020-00682-w"},{"key":"27_CR20","doi-asserted-by":"publisher","unstructured":"Mlynarski, P., Delingette, H., Criminisi, A., Ayache, N.: Deep learning with mixed supervision for brain tumor segmentation. J. Med. Imaging 6(3), 034002 (2019). https:\/\/doi.org\/10.1117\/1.JMI.6.3.034002. https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-medical-imaging\/volume-6\/issue-3\/034002\/Deep-learning-with-mixed-supervision-for-brain-tumor-segmentation\/10.1117\/1.JMI.6.3.034002.full","DOI":"10.1117\/1.JMI.6.3.034002"},{"key":"27_CR21","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Yosinski, J., Clune, J.: Understanding neural networks via feature visualization: a survey. arXiv:1904.08939 [cs, stat] (2019)","DOI":"10.1007\/978-3-030-28954-6_4"},{"issue":"4","key":"27_CR22","first-page":"291","volume":"23","author":"AC Ruifrok","year":"2001","unstructured":"Ruifrok, A.C., Johnston, D.A.: Quantification of histochemical staining by color deconvolution. Anal. Quant. Cytol. Histol. 23(4), 291\u2013299 (2001)","journal-title":"Anal. Quant. Cytol. Histol."},{"key":"27_CR23","doi-asserted-by":"publisher","unstructured":"Saillard, C., et al.: Identification of pancreatic adenocarcinoma molecular subtypes on histology slides using deep learning models. J. Clin. Oncol. 39(15_Suppl.), 4141 (2021). https:\/\/doi.org\/10.1200\/JCO.2021.39.15suppl.4141. https:\/\/ascopubs.org\/doi\/abs\/10.1200\/JCO.2021.39.15suppl.4141","DOI":"10.1200\/JCO.2021.39.15suppl.4141"},{"key":"27_CR24","unstructured":"Tourniaire, P., Ilie, M., Hofman, P., Ayache, N., Delingette, H.: Attention-based multiple instance learning with mixed supervision on the camelyon16 dataset. In: Proceedings of the MICCAI Workshop on Computational Pathology, pp. 216\u2013226. PMLR (2021). https:\/\/proceedings.mlr.press\/v156\/tourniaire21a.html"},{"key":"27_CR25","unstructured":"Tu, H.H., Lin, H.T.: One-sided support vector regression for multiclass cost-sensitive classification, p. 8 (2010)"},{"key":"27_CR26","doi-asserted-by":"crossref","unstructured":"Weitz, P., et al.: Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression based convolutional neural networks. arXiv preprint arXiv:2104.09310 (2021)","DOI":"10.1093\/bioinformatics\/btac343"},{"key":"27_CR27","unstructured":"WHO: Colposcopy and treatment of cervical intraepithelial neoplasia: a beginners\u2019 manual (2020). https:\/\/screening.iarc.fr\/colpochap.php?chap=2"}],"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_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T13:07:23Z","timestamp":1709816843000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25082-8_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250811","9783031250828"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25082-8_27","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":"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)"}}]}}