{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T07:53:29Z","timestamp":1753602809091,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030872366"},{"type":"electronic","value":"9783030872373"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-87237-3_27","type":"book-chapter","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T06:19:41Z","timestamp":1632377981000},"page":"277-287","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Generalizing Nucleus Recognition Model in Multi-source Ki67 Immunohistochemistry Stained Images via Domain-Specific Pruning"],"prefix":"10.1007","author":[{"given":"Jiatong","family":"Cai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenglu","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Can","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Honglin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shichuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"27_CR1","unstructured":"Albuquerque, I., Monteiro, J., Darvishi, M., Falk, T.H., Mitliagkas, I.: Generalizing to unseen domains via distribution matching. arXiv preprint arXiv:1911.00804 (2019)"},{"key":"27_CR2","unstructured":"Arjovsky, M., Bottou, L., Gulrajani, I., Lopez-Paz, D.: Invariant risk minimization. arXiv preprint arXiv:1907.02893 (2019)"},{"key":"27_CR3","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.ejca.2017.07.041","volume":"84","author":"CM Focke","year":"2017","unstructured":"Focke, C.M., et al.: Interlaboratory variability of ki67 staining in breast cancer. Eur. J. Cancer 84, 219\u2013227 (2017)","journal-title":"Eur. J. Cancer"},{"key":"27_CR4","unstructured":"Frankle, J., Carbin, M.: The lottery ticket hypothesis: finding sparse, trainable neural networks. arXiv preprint arXiv:1803.03635 (2018)"},{"issue":"1","key":"27_CR5","first-page":"2030","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2030\u20132096 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"27_CR6","unstructured":"Gulrajani, I., Lopez-Paz, D.: In search of lost domain generalization. arXiv preprint arXiv:2007.01434 (2020)"},{"key":"27_CR7","doi-asserted-by":"crossref","unstructured":"Hashimoto, N., et al.: Multi-scale domain-adversarial multiple-instance CNN for cancer subtype classification with unannotated histopathological images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3852\u20133861 (2020)","DOI":"10.1109\/CVPR42600.2020.00391"},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3","key":"27_CR9","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/s00428-017-2258-0","volume":"472","author":"G Kl\u00f6ppel","year":"2018","unstructured":"Kl\u00f6ppel, G., La Rosa, S.: Ki67 labeling index: assessment and prognostic role in gastroenteropancreatic neuroendocrine neoplasms. Virchows Arch. 472(3), 341\u2013349 (2018)","journal-title":"Virchows Arch."},{"issue":"1\u20132","key":"27_CR10","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1002\/nav.3800020109","volume":"2","author":"HW Kuhn","year":"1955","unstructured":"Kuhn, H.W.: The hungarian method for the assignment problem. Naval Res. Logistics Q. 2(1\u20132), 83\u201397 (1955)","journal-title":"Naval Res. Logistics Q."},{"key":"27_CR11","doi-asserted-by":"publisher","first-page":"162","DOI":"10.3389\/fmed.2019.00162","volume":"6","author":"MW Lafarge","year":"2019","unstructured":"Lafarge, M.W., Pluim, J.P., Eppenhof, K.A., Veta, M.: Learning domain-invariant representations of histological images. Front. Med. 6, 162 (2019)","journal-title":"Front. Med."},{"key":"27_CR12","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, Y., Song, Y.Z., Hospedales, T.: Learning to generalize: meta-learning for domain generalization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11596"},{"key":"27_CR13","unstructured":"Mahajan, D., Tople, S., Sharma, A.: Domain generalization using causal matching. arXiv preprint arXiv:2006.07500 (2020)"},{"key":"27_CR14","doi-asserted-by":"crossref","unstructured":"Reis-Filho, J.S., Davidson, N.E.: Ki67 assessment in breast cancer: are we there yet?. JNCI: J. Natl. Cancer Inst. (2020)","DOI":"10.1093\/jnci\/djaa202"},{"issue":"1","key":"27_CR15","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1038\/s41379-018-0109-4","volume":"32","author":"DL Rimm","year":"2019","unstructured":"Rimm, D.L., et al.: An international multicenter study to evaluate reproducibility of automated scoring for assessment of ki67 in breast cancer. Mod. Pathol. 32(1), 59\u201369 (2019)","journal-title":"Mod. Pathol."},{"key":"27_CR16","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":"27_CR17","unstructured":"Sagawa, S., Koh, P.W., Hashimoto, T.B., Liang, P.: Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. arXiv preprint arXiv:1911.08731 (2019)"},{"key":"27_CR18","doi-asserted-by":"publisher","first-page":"101654","DOI":"10.1016\/j.media.2020.101654","volume":"61","author":"Y Shem","year":"2020","unstructured":"Shem, Y., et al.: Domain-invariant interpretable fundus image quality assessment. Med. Image Anal. 61, 101654 (2020)","journal-title":"Med. Image Anal."},{"key":"27_CR19","doi-asserted-by":"crossref","unstructured":"Sikaroudi, M., Ghojogh, B., Karray, F., Crowley, M., Tizhoosh, H.: Magnification generalization for histopathology image embedding. arXiv preprint arXiv:2101.07757 (2021)","DOI":"10.1109\/ISBI48211.2021.9433978"},{"issue":"5","key":"27_CR20","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"},{"key":"27_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/978-3-319-49409-8_35","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"B Sun","year":"2016","unstructured":"Sun, B., Saenko, K.: Deep CORAL: correlation alignment for deep domain adaptation. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 443\u2013450. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49409-8_35"},{"key":"27_CR22","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7167\u20137176 (2017)","DOI":"10.1109\/CVPR.2017.316"},{"issue":"5","key":"27_CR23","doi-asserted-by":"publisher","first-page":"988","DOI":"10.1109\/72.788640","volume":"10","author":"VN Vapnik","year":"1999","unstructured":"Vapnik, V.N.: An overview of statistical learning theory. IEEE Trans. Neural Netw. 10(5), 988\u2013999 (1999)","journal-title":"IEEE Trans. Neural Netw."},{"key":"27_CR24","unstructured":"Volpi, R., Namkoong, H., Sener, O., Duchi, J., Murino, V., Savarese, S.: Generalizing to unseen domains via adversarial data augmentation. arXiv preprint arXiv:1805.12018 (2018)"},{"key":"27_CR25","doi-asserted-by":"crossref","unstructured":"Volynskaya, Z., Mete, O., Pakbaz, S., Al-Ghamdi, D., Asa, S.L.: Ki67 quantitative interpretation: insights using image analysis. J. Pathol. Inf. 10 (2019)","DOI":"10.4103\/jpi.jpi_76_18"},{"key":"27_CR26","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.media.2017.07.003","volume":"44","author":"Y Xie","year":"2018","unstructured":"Xie, Y., Xing, F., Shi, X., Kong, X., Su, H., Yang, L.: Efficient and robust cell detection: a structured regression approach. Med. Image Anal. 44, 245\u2013254 (2018)","journal-title":"Med. Image Anal."},{"key":"27_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-030-32239-7_82","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"F Xing","year":"2019","unstructured":"Xing, F., Bennett, T., Ghosh, D.: Adversarial domain adaptation and pseudo-labeling for cross-modality microscopy image quantification. In: Shen, D. (ed.) MICCAI 2019. LNCS, vol. 11764, pp. 740\u2013749. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_82"},{"issue":"11","key":"27_CR28","doi-asserted-by":"publisher","first-page":"3088","DOI":"10.1109\/TBME.2019.2900378","volume":"66","author":"F Xing","year":"2019","unstructured":"Xing, F., Cornish, T.C., Bennett, T., Ghosh, D., Yang, L.: Pixel-to-pixel learning with weak supervision for single-stage nucleus recognition in ki67 images. IEEE Trans. Biomed. Eng. 66(11), 3088\u20133097 (2019)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"5","key":"27_CR29","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1007\/s12094-017-1774-3","volume":"20","author":"C Yang","year":"2018","unstructured":"Yang, C., et al.: Ki67 targeted strategies for cancer therapy. Clin. Transl. Oncol. 20(5), 570\u2013575 (2018)","journal-title":"Clin. Transl. Oncol."},{"key":"27_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1007\/978-3-030-59713-9_45","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"D Yang","year":"2020","unstructured":"Yang, D., Yang, Y., Huang, T., Wu, B., Wang, L., Xu, Yanwu: Residual-cyclegan based camera adaptation for robust diabetic retinopathy screening. In: Martel, A.L. (ed.) MICCAI 2020. LNCS, vol. 12262, pp. 464\u2013474. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59713-9_45"},{"key":"27_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/978-3-030-32245-8_29","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"J Yang","year":"2019","unstructured":"Yang, J., Dvornek, N.C., Zhang, F., Chapiro, J., Lin, M., Duncan, J.S.: Unsupervised domain adaptation via disentangled representations: application to cross-modality liver segmentation. In: Shen, D. (ed.) MICCAI 2019. LNCS, vol. 11765, pp. 255\u2013263. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_29"},{"issue":"2","key":"27_CR32","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/S1470-2045(09)70262-1","volume":"11","author":"R Yerushalmi","year":"2010","unstructured":"Yerushalmi, R., Woods, R., Ravdin, P.M., Hayes, M.M., Gelmon, K.A.: Ki67 in breast cancer: prognostic and predictive potential. Lancet Oncol. 11(2), 174\u2013183 (2010)","journal-title":"Lancet Oncol."},{"issue":"7","key":"27_CR33","doi-asserted-by":"publisher","first-page":"2531","DOI":"10.1109\/TMI.2020.2973595","volume":"39","author":"L Zhang","year":"2020","unstructured":"Zhang, L., et al.: Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation. IEEE Trans. Med. Imaging 39(7), 2531\u20132540 (2020)","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87237-3_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:37:23Z","timestamp":1673311043000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87237-3_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872366","9783030872373"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87237-3_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.org\/en\/","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":"1622","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":"531","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":"33% - 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":"4","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":"The conference was held virtually.","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)"}}]}}