{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T15:06:44Z","timestamp":1768316804585,"version":"3.49.0"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030972806","type":"print"},{"value":"9783030972813","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-030-97281-3_2","type":"book-chapter","created":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T17:03:51Z","timestamp":1646154231000},"page":"14-22","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Assessing Domain Adaptation Techniques for\u00a0Mitosis Detection in\u00a0Multi-scanner Breast Cancer Histopathology Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9020-3383","authenticated-orcid":false,"given":"Jack","family":"Breen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4385-3153","authenticated-orcid":false,"given":"Kieran","family":"Zucker","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0890-0399","authenticated-orcid":false,"given":"Nicolas M.","family":"Orsi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0134-107X","authenticated-orcid":false,"given":"Nishant","family":"Ravikumar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,2]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","unstructured":"Aubreville, M., et al.: Mitosis domain generalization challenge (2021). https:\/\/doi.org\/10.5281\/zenodo.4573978","DOI":"10.5281\/zenodo.4573978"},{"key":"2_CR2","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1109\/TMI.2017.2781228","volume":"37","author":"A BenTaieb","year":"2018","unstructured":"BenTaieb, A., Hamarneh, G.: Adversarial stain transfer for histopathology image analysis. IEEE Trans. Med. Imaging 37, 792\u2013802 (2018). https:\/\/doi.org\/10.1109\/TMI.2017.2781228","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2_CR3","doi-asserted-by":"publisher","unstructured":"Bertram, C.A., et al.: Computer-assisted mitotic count using a deep learning-based algorithm improves inter-observer reproducibility and accuracy in canine cutaneous mast cell tumors (2021). https:\/\/doi.org\/10.1101\/2021.06.04.446287","DOI":"10.1101\/2021.06.04.446287"},{"key":"2_CR4","doi-asserted-by":"publisher","unstructured":"Ganesh, A., Vasanth, N.R., George, K.: Staining of histopathology slides using image style transfer algorithm (2019). https:\/\/doi.org\/10.1109\/SSCI.2018.8628672","DOI":"10.1109\/SSCI.2018.8628672"},{"key":"2_CR5","doi-asserted-by":"publisher","unstructured":"Gatys, L., Ecker, A., Bethge, M.: A neural algorithm of artistic style. J. Vis. 16 (2016). https:\/\/doi.org\/10.1167\/16.12.326","DOI":"10.1167\/16.12.326"},{"key":"2_CR6","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local nash equilibrium, vol. 2017-December (2017)"},{"key":"2_CR7","doi-asserted-by":"publisher","unstructured":"Izadyyazdanabadi, M., et al.: Fluorescence image histology pattern transformation using image style transfer. Front. Oncol. 9 (2019). https:\/\/doi.org\/10.3389\/fonc.2019.00519","DOI":"10.3389\/fonc.2019.00519"},{"key":"2_CR8","doi-asserted-by":"publisher","first-page":"1729","DOI":"10.1109\/TBME.2014.2303294","volume":"61","author":"AM Khan","year":"2014","unstructured":"Khan, A.M., Rajpoot, N., Treanor, D., Magee, D.: A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans. Biomed. Eng. 61, 1729\u20131738 (2014). https:\/\/doi.org\/10.1109\/TBME.2014.2303294","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"2_CR9","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.humpath.2020.09.009","volume":"106","author":"P Laflamme","year":"2020","unstructured":"Laflamme, P., et al.: Phospho-histone-H3 immunostaining for pulmonary carcinoids: impact on clinical appraisal, interobserver correlation, and diagnostic processing efficiency. Hum. Pathol. 106, 74\u201381 (2020). https:\/\/doi.org\/10.1016\/j.humpath.2020.09.009","journal-title":"Hum. Pathol."},{"key":"2_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-019-2605-z","volume":"20","author":"K Liimatainen","year":"2019","unstructured":"Liimatainen, K., Kananen, L., Latonen, L., Ruusuvuori, P.: Iterative unsupervised domain adaptation for generalized cell detection from brightfield z-stacks. BMC Bioinform. 20, 1\u201310 (2019). https:\/\/doi.org\/10.1186\/s12859-019-2605-z","journal-title":"BMC Bioinform."},{"key":"2_CR11","doi-asserted-by":"publisher","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42 (2020). https:\/\/doi.org\/10.1109\/TPAMI.2018.2858826","DOI":"10.1109\/TPAMI.2018.2858826"},{"key":"2_CR12","doi-asserted-by":"publisher","unstructured":"Macenko, M., et al.: A method for normalizing histology slides for quantitative analysis (2009). https:\/\/doi.org\/10.1109\/ISBI.2009.5193250","DOI":"10.1109\/ISBI.2009.5193250"},{"key":"2_CR13","doi-asserted-by":"publisher","first-page":"9795","DOI":"10.1038\/s41598-020-65958-2","volume":"10","author":"C Marzahl","year":"2020","unstructured":"Marzahl, C., et al.: Deep learning-based quantification of pulmonary hemosiderophages in cytology slides. Sci. Rep. 10, 9795 (2020). https:\/\/doi.org\/10.1038\/s41598-020-65958-2","journal-title":"Sci. Rep."},{"key":"2_CR14","doi-asserted-by":"publisher","unstructured":"Mercan, C., et al.: Virtual staining for mitosis detection in breast histopathology. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1770\u20131774 (2020). https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098409","DOI":"10.1109\/ISBI45749.2020.9098409"},{"key":"2_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1007\/978-3-030-59722-1_32","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"H Nishar","year":"2020","unstructured":"Nishar, H., Chavanke, N., Singhal, N.: Histopathological stain transfer using style transfer network with adversarial loss. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 330\u2013340. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59722-1_32"},{"key":"2_CR16","doi-asserted-by":"publisher","unstructured":"Ot\u00e1lora, S., Atzori, M., Andrearczyk, V., Khan, A., M\u00fcller, H.: Staining invariant features for improving generalization of deep convolutional neural networks in computational pathology. Front. Bioeng. Biotechnol. 7 (2019). https:\/\/doi.org\/10.3389\/fbioe.2019.00198","DOI":"10.3389\/fbioe.2019.00198"},{"key":"2_CR17","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":"2_CR18","unstructured":"Royal College of Pathologists: Meeting pathology demand: Histopathology workforce census (2018)"},{"key":"2_CR19","doi-asserted-by":"publisher","unstructured":"Shin, S.J., et al.: Style transfer strategy for developing a generalizable deep learning application in digital pathology. Comput. Methods Programs Biomed. 198 (2021). https:\/\/doi.org\/10.1016\/j.cmpb.2020.105815","DOI":"10.1016\/j.cmpb.2020.105815"},{"key":"2_CR20","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung, H., et al.: Global cancer statistics 2020: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209\u2013249 (2021). https:\/\/doi.org\/10.3322\/caac.21660","journal-title":"CA Cancer J. Clin."},{"key":"2_CR21","unstructured":"World Health Organization: WHO Classification of Breast Tumours, vol. 2 (2019)"},{"key":"2_CR22","doi-asserted-by":"publisher","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.244","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Lecture Notes in Computer Science","Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-97281-3_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T17:05:56Z","timestamp":1646154356000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-97281-3_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030972806","9783030972813"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-97281-3_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"2 March 2022","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)"}}]}}