{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T14:07:18Z","timestamp":1742998038576,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"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_26","type":"book-chapter","created":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T09:12:42Z","timestamp":1676106762000},"page":"397-407","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["CCRL: Contrastive Cell Representation Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6463-4465","authenticated-orcid":false,"given":"Ramin","family":"Nakhli","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amirali","family":"Darbandsari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hossein","family":"Farahani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4212-7224","authenticated-orcid":false,"given":"Ali","family":"Bashashati","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,12]]},"reference":[{"issue":"3","key":"26_CR1","doi-asserted-by":"publisher","first-page":"72","DOI":"10.5539\/gjhs.v8n3p72","volume":"8","author":"HA Alturkistani","year":"2016","unstructured":"Alturkistani, H.A., Tashkandi, F.M., Mohammedsaleh, Z.M.: Histological stains: a literature review and case study. Glob. J. Health Sci. 8(3), 72 (2016)","journal-title":"Glob. J. Health Sci."},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Amgad, M., et al.: NuCLS: a scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation. arXiv preprint arXiv:2102.09099 (2021)","DOI":"10.1093\/gigascience\/giac037"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Azizi, S., et al.: Big self-supervised models advance medical image classification. arXiv preprint arXiv:2101.05224 (2021)","DOI":"10.1109\/ICCV48922.2021.00346"},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Bhaskar, D., et al.: A methodology for morphological feature extraction and unsupervised cell classification. bioRxiv, p. 623793 (2019)","DOI":"10.1101\/623793"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Boyle, P., Langman, M.J.: ABC of colorectal cancer: epidemiology. BMJ 321(Suppl. S6) (2000)","DOI":"10.1136\/sbmj.0012452"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Caron, M., et al.: Emerging properties in self-supervised vision transformers. arXiv preprint arXiv:2104.14294 (2021)","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"26_CR7","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"key":"26_CR8","unstructured":"Chen, T., Kornblith, S., Swersky, K., Norouzi, M., Hinton, G.: Big self-supervised models are strong semi-supervised learners. arXiv preprint arXiv:2006.10029 (2020)"},{"key":"26_CR9","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, pp. 2180\u20132188 (2016)"},{"key":"26_CR10","unstructured":"Chen, X., Fan, H., Girshick, R., He, K.: Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297 (2020)"},{"key":"26_CR11","volume":"7","author":"O Ciga","year":"2022","unstructured":"Ciga, O., Xu, T., Martel, A.L.: Self supervised contrastive learning for digital histopathology. Mach. Learn. Appl. 7, 100198 (2022)","journal-title":"Mach. Learn. Appl."},{"key":"26_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1007\/978-3-642-40763-5_50","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013","author":"AA Cruz-Roa","year":"2013","unstructured":"Cruz-Roa, A.A., Arevalo Ovalle, J.E., Madabhushi, A., Gonz\u00e1lez Osorio, F.A.: A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 403\u2013410. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40763-5_50"},{"key":"26_CR13","unstructured":"Dalle, J.R., Li, H., Huang, C.H., Leow, W.K., Racoceanu, D., Putti, T.C.: Nuclear pleomorphism scoring by selective cell nuclei detection. In: WACV (2009)"},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"26_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101563","volume":"58","author":"S Graham","year":"2019","unstructured":"Graham, S., et al.: Hover-net: simultaneous segmentation and classification of nuclei in multi-tissue histology images. Med. Image Anal. 58, 101563 (2019)","journal-title":"Med. Image Anal."},{"key":"26_CR16","unstructured":"Grill, J.B., et al.: Bootstrap your own latent: a new approach to self-supervised learning. arXiv preprint arXiv:2006.07733 (2020)"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"26_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630\u2013645. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38"},{"issue":"4","key":"26_CR19","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1038\/labinvest.2014.155","volume":"95","author":"A Heindl","year":"2015","unstructured":"Heindl, A., Nawaz, S., Yuan, Y.: Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology. Lab. Invest. 95(4), 377\u2013384 (2015)","journal-title":"Lab. Invest."},{"issue":"3","key":"26_CR20","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.1109\/JBHI.2018.2852639","volume":"23","author":"B Hu","year":"2018","unstructured":"Hu, B., et al.: Unsupervised learning for cell-level visual representation in histopathology images with generative adversarial networks. IEEE J. Biomed. Health Inform. 23(3), 1316\u20131328 (2018)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"26_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101696","volume":"63","author":"S Javed","year":"2020","unstructured":"Javed, S., et al.: Cellular community detection for tissue phenotyping in colorectal cancer histology images. Med. Image Anal. 63, 101696 (2020)","journal-title":"Med. Image Anal."},{"key":"26_CR22","unstructured":"Komodakis, N., Gidaris, S.: Unsupervised representation learning by predicting image rotations. In: International Conference on Learning Representations (ICLR) (2018)"},{"issue":"1","key":"26_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-75708-z","volume":"10","author":"A Levy-Jurgenson","year":"2020","unstructured":"Levy-Jurgenson, A., Tekpli, X., Kristensen, V.N., Yakhini, Z.: Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer. Sci. Rep. 10(1), 1\u201311 (2020)","journal-title":"Sci. Rep."},{"key":"26_CR24","unstructured":"Liu, H., HaoChen, J.Z., Gaidon, A., Ma, T.: Self-supervised learning is more robust to dataset imbalance. arXiv preprint arXiv:2110.05025 (2021)"},{"key":"26_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/978-3-030-87444-5_10","volume-title":"Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data","author":"P Martin-Gonzalez","year":"2021","unstructured":"Martin-Gonzalez, P., Crispin-Ortuzar, M., Markowetz, F.: Predictive modelling of highly multiplexed tumour tissue images by graph neural networks. In: Reyes, M., et al. (eds.) IMIMIC\/TDA4MedicalData -2021. LNCS, vol. 12929, pp. 98\u2013107. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87444-5_10"},{"key":"26_CR26","doi-asserted-by":"crossref","unstructured":"van Muijen, G.N., et al.: Cell type heterogeneity of cytokeratin expression in complex epithelia and carcinomas as demonstrated by monoclonal antibodies specific for cytokeratins nos. 4 and 13. Exp. Cell Res. 162(1), 97\u2013113 (1986)","DOI":"10.1016\/0014-4827(86)90429-5"},{"key":"26_CR27","doi-asserted-by":"crossref","unstructured":"Nguyen, K., Jain, A.K., Sabata, B.: Prostate cancer detection: fusion of cytological and textural features. J. Pathol. Inform. 2 (2011)","DOI":"10.4103\/2153-3539.92030"},{"key":"26_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/978-3-319-46466-4_5","volume-title":"Computer Vision \u2013 ECCV 2016","author":"M Noroozi","year":"2016","unstructured":"Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 69\u201384. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46466-4_5"},{"issue":"5","key":"26_CR29","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1109\/TMI.2016.2525803","volume":"35","author":"K Sirinukunwattana","year":"2016","unstructured":"Sirinukunwattana, K., Raza, S.E.A., Tsang, Y.W., Snead, D.R., Cree, I.A., Rajpoot, N.M.: Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35(5), 1196\u20131206 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"26_CR30","doi-asserted-by":"publisher","first-page":"3933","DOI":"10.18632\/oncotarget.13907","volume":"8","author":"B Son","year":"2017","unstructured":"Son, B., Lee, S., Youn, H., Kim, E., Kim, W., Youn, B.: The role of tumor microenvironment in therapeutic resistance. Oncotarget 8(3), 3933 (2017)","journal-title":"Oncotarget"},{"issue":"9","key":"26_CR31","doi-asserted-by":"publisher","first-page":"2717","DOI":"10.3390\/s20092717","volume":"20","author":"C Vununu","year":"2020","unstructured":"Vununu, C., Lee, S.H., Kwon, K.R.: A strictly unsupervised deep learning method for hep-2 cell image classification. Sensors 20(9), 2717 (2020)","journal-title":"Sensors"},{"key":"26_CR32","doi-asserted-by":"crossref","unstructured":"Xie, E., et al.: DetCo: unsupervised contrastive learning for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8392\u20138401 (2021)","DOI":"10.1109\/ICCV48922.2021.00828"},{"key":"26_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/978-3-030-59722-1_33","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"X Xie","year":"2020","unstructured":"Xie, X., Chen, J., Li, Y., Shen, L., Ma, K., Zheng, Y.: Instance-aware self-supervised learning for nuclei segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 341\u2013350. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59722-1_33"},{"key":"26_CR34","unstructured":"Zhang, L., Amgad, M., Cooper, L.A.: A histopathology study comparing contrastive semi-supervised and fully supervised learning. arXiv preprint arXiv:2111.05882 (2021)"},{"key":"26_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1007\/978-3-319-46487-9_40","volume-title":"Computer Vision \u2013 ECCV 2016","author":"R Zhang","year":"2016","unstructured":"Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 649\u2013666. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_40"}],"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_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T13:07:10Z","timestamp":1709816830000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25082-8_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250811","9783031250828"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25082-8_26","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)"}}]}}