{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:13:32Z","timestamp":1762956812013,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030972806"},{"type":"electronic","value":"9783030972813"}],"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_7","type":"book-chapter","created":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T17:03:51Z","timestamp":1646154231000},"page":"53-57","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MitoDet: Simple and\u00a0Robust Mitosis Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0617-0460","authenticated-orcid":false,"given":"Jakob","family":"Dexl","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0625-4418","authenticated-orcid":false,"given":"Michaela","family":"Benz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7693-3542","authenticated-orcid":false,"given":"Volker","family":"Bruns","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2578-7754","authenticated-orcid":false,"given":"Petr","family":"Kuritcyn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0840-8695","authenticated-orcid":false,"given":"Thomas","family":"Wittenberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,2]]},"reference":[{"key":"7_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":"7_CR2","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.: RandAugment: practical automated data augmentation with a reduced search space. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 18613\u201318624. Curran Associates, Inc. (2020)","DOI":"10.1109\/CVPRW50498.2020.00359"},{"issue":"59","key":"7_CR3","first-page":"1","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(59), 1\u201335 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"7_CR4","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., Wang, X., Liu, W., Latecki, L.J.: DeepMitosis: mitosis detection via deep detection, verification and segmentation networks. Med. Image Anal. 45, 121\u2013133 (2018). https:\/\/doi.org\/10.1016\/j.media.2017.12.002","journal-title":"Med. Image Anal."},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"issue":"1","key":"7_CR6","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(1), 9795 (2020). https:\/\/doi.org\/10.1038\/s41598-020-65958-2","journal-title":"Sci. Rep."},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"M\u00fcller, S.G., Hutter, F.: TrivialAugment: tuning-free yet state-of-the-art data augmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 774\u2013782 (2021)","DOI":"10.1109\/ICCV48922.2021.00081"},{"key":"7_CR8","doi-asserted-by":"publisher","unstructured":"Smith, L.N., Topin, N.: Super-convergence: very fast training of neural networks using large learning rates. In: Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, vol. 11006, pp. 369\u2013386. SPIE, May 2019. https:\/\/doi.org\/10.1117\/12.2520589","DOI":"10.1117\/12.2520589"},{"key":"7_CR9","unstructured":"Sohn, K., Zhang, Z., Li, C.L., Zhang, H., Lee, C.Y., Pfister, T.: A Simple Semi-Supervised Learning Framework for Object Detection. arXiv:2005.04757 [cs], December 2020"},{"issue":"2","key":"7_CR10","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1109\/JBHI.2020.3032060","volume":"25","author":"K Stacke","year":"2021","unstructured":"Stacke, K., Eilertsen, G., Unger, J., Lundstrom, C.: Measuring domain shift for deep learning in histopathology. IEEE J. Biomed. Health Inform. 25(2), 325\u2013336 (2021). https:\/\/doi.org\/10.1109\/JBHI.2020.3032060","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"7_CR11","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: Proceedings of the 36th International Conference on Machine Learning, pp. 6105\u20136114. PMLR, May 2019. ISSN 2640-3498"},{"issue":"9","key":"7_CR12","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 H&E breast histology using PHH3 as a reference to train distilled stain-invariant convolutional networks. IEEE Trans. Med. Imaging 37(9), 2126\u20132136 (2018). https:\/\/doi.org\/10.1109\/TMI.2018.2820199","journal-title":"IEEE Trans. Med. Imaging"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Xie, Q., Luong, M.T., Hovy, E., Le, Q.V.: Self-training with noisy student improves ImageNet classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10687\u201310698 (2020)","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"7_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1007\/978-3-030-32226-7_45","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"M Zlocha","year":"2019","unstructured":"Zlocha, M., Dou, Q., Glocker, B.: Improving RetinaNet for CT lesion detection with dense masks from weak RECIST labels. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 402\u2013410. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_45"}],"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_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T17:06:33Z","timestamp":1646154393000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-97281-3_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030972806","9783030972813"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-97281-3_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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)"}}]}}