{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T17:54:36Z","timestamp":1760550876312},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030239367"},{"type":"electronic","value":"9783030239374"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-23937-4_16","type":"book-chapter","created":{"date-parts":[[2019,7,2]],"date-time":"2019-07-02T22:59:32Z","timestamp":1562108372000},"page":"135-143","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Fast Pyramidal Bayesian Model for Mitosis Detection in Whole-Slide Images"],"prefix":"10.1007","author":[{"given":"Santiago","family":"L\u00f3pez-Tapia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9","family":"Aneiros-Fern\u00e1ndez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicol\u00e1s","family":"P\u00e9rez\u00a0de\u00a0la\u00a0Blanca","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,7,3]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Chen, H., Dou, Q., Wang, X., Qin, J., Heng, P.A.: Mitosis detection in breast cancer histology images via deep cascaded networks. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), pp. 1160\u20131166 (2016)","DOI":"10.1609\/aaai.v30i1.10140"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Chen, H., Wang, X., Heng, P.A.: Automated mitosis detection with deep regression networks. In: IEEE International Symposium on Biomedical Imaging, pp. 1204\u20131207 (2016)","DOI":"10.1109\/ISBI.2016.7493482"},{"key":"16_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1007\/978-3-642-40763-5_51","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013","author":"DC Cire\u015fan","year":"2013","unstructured":"Cire\u015fan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 411\u2013418. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40763-5_51"},{"key":"16_CR4","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: Proceedings of the 33rd International Conference on Machine Learning (ICML-16) (2016)"},{"key":"16_CR5","unstructured":"ICPR (2014). https:\/\/mitos-atypia-14.grand-challenge.org\/"},{"key":"16_CR6","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, vol. 28. pp. 2017\u20132025 (2015)"},{"key":"16_CR7","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs\/1412.6980 (2014). http:\/\/arxiv.org\/abs\/1412.6980"},{"key":"16_CR8","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.media.2017.12.002","volume":"45","author":"C Li","year":"2018","unstructured":"Li, C., Wanga, X., Liua, W., Latecki, L.J.: DeepMitosis: mitosis detection via deep detection, verification and segmentation networks. Med. Image Anal. 45, 121\u2013133 (2018)","journal-title":"Med. Image Anal."},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Macenko, M., et al.: A method for normalizing histology slides for quantitative analysis. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1107\u20131110 (2009)","DOI":"10.1109\/ISBI.2009.5193250"},{"key":"16_CR10","unstructured":"MICCAI (2016). http:\/\/tupac.tue-image.nl\/"},{"key":"16_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1007\/978-3-319-24571-3_12","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"A Paul","year":"2015","unstructured":"Paul, A., Dey, A., Mukherjee, D.P., Sivaswamy, J., Tourani, V.: Regenerative random forest with automatic feature selection to detect mitosis in histopathological breast cancer images. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 94\u2013102. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24571-3_12"},{"issue":"5","key":"16_CR12","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/38.946629","volume":"21","author":"E Reinhard","year":"2001","unstructured":"Reinhard, E., Ashikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Comput. Graph. Appl. 21(5), 34\u201341 (2001)","journal-title":"IEEE Comput. Graph. Appl."},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Roux, L., et al.: Mitosis detection in breast cancer histological images an ICPR 2012 contest. J. Pathol. Inform. (2013)","DOI":"10.4103\/2153-3539.112693"},{"issue":"12","key":"16_CR14","doi-asserted-by":"publisher","first-page":"2025","DOI":"10.1111\/jdv.14485","volume":"31","author":"A Tejera-Vaquerizo","year":"2017","unstructured":"Tejera-Vaquerizo, A., et al.: Is mitotic rate still useful in the management of patients with thin melanoma? J. Eur. Acad. Dermatol. Venereol. 31(12), 2025\u20132029 (2017)","journal-title":"J. Eur. Acad. Dermatol. Venereol."},{"issue":"9","key":"16_CR15","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 \u201cH&E\u201d 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"},{"issue":"1","key":"16_CR16","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.media.2014.11.010","volume":"20","author":"M Veta","year":"2015","unstructured":"Veta, M., et al.: Assessment of algorithms for mitosis detection in breast cancer histopathology images. Med. Image Anal. 20(1), 237\u2013248 (2015)","journal-title":"Med. Image Anal."},{"key":"16_CR17","doi-asserted-by":"publisher","first-page":"034003","DOI":"10.1117\/1.JMI.1.3.034003","volume":"1","author":"H Wang","year":"2014","unstructured":"Wang, H., et al.: Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. J. Med. Imaging 1, 034003 (2014)","journal-title":"J. Med. Imaging"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Proceedings of the British Machine Vision Conference (BMVC), pp. 87.1\u201387.12, September 2016","DOI":"10.5244\/C.30.87"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Zerhouni, E., L\u00e1nyi, D., Viana, M., Gabrani, M.: Wide residual networks for mitosis detection. In: IEEE 14th International Symposium on Biomedical Imaging (ISBI), pp. 924\u2013928 (2017)","DOI":"10.1109\/ISBI.2017.7950667"}],"container-title":["Lecture Notes in Computer Science","Digital Pathology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-23937-4_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T20:00:49Z","timestamp":1663876849000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-23937-4_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030239367","9783030239374"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-23937-4_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"3 July 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECDP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Congress on Digital Pathology","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Warwick","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 April 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 April 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecdp2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.ecdp2019.org\/","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":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30","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":"21","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":"70% - 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":"3","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)"}}]}}