{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:53:16Z","timestamp":1742975596509,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030804312"},{"type":"electronic","value":"9783030804329"}],"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\/978-3-030-80432-9_25","type":"book-chapter","created":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T23:08:25Z","timestamp":1625526505000},"page":"322-336","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning-Based Bias Transfer for Overcoming Laboratory Differences of Microscopic Images"],"prefix":"10.1007","author":[{"given":"Ann-Katrin","family":"Thebille","sequence":"first","affiliation":[]},{"given":"Esther","family":"Dietrich","sequence":"additional","affiliation":[]},{"given":"Martin","family":"Klaus","sequence":"additional","affiliation":[]},{"given":"Lukas","family":"Gernhold","sequence":"additional","affiliation":[]},{"given":"Maximilian","family":"Lennartz","sequence":"additional","affiliation":[]},{"given":"Christoph","family":"Kuppe","sequence":"additional","affiliation":[]},{"given":"Rafael","family":"Kramann","sequence":"additional","affiliation":[]},{"given":"Tobias B.","family":"Huber","sequence":"additional","affiliation":[]},{"given":"Guido","family":"Sauter","sequence":"additional","affiliation":[]},{"given":"Victor G.","family":"Puelles","sequence":"additional","affiliation":[]},{"given":"Marina","family":"Zimmermann","sequence":"additional","affiliation":[]},{"given":"Stefan","family":"Bonn","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,6]]},"reference":[{"key":"25_CR1","doi-asserted-by":"crossref","unstructured":"Armanious, K., Tanwar, A., Abdulatif, S., K\u00fcstner, T., Gatidis, S., Yang, B.: Unsupervised adversarial correction of rigid mr motion artifacts. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1494\u20131498 (2020)","DOI":"10.1109\/ISBI45749.2020.9098570"},{"key":"25_CR2","doi-asserted-by":"crossref","unstructured":"Arvaniti, E., et al.: Replication Data for: Automated Gleason grading of prostate cancer tissue microarrays via deep learning (2018)","DOI":"10.1101\/280024"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Arvaniti, E., et al.: Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Scientific Reports (2018)","DOI":"10.1101\/280024"},{"key":"25_CR4","unstructured":"de Bel, T., Hermsen, M., Jesper Kers, R., van der Laak, J., Litjens, G.: Stain-transforming cycle-consistent generative adversarial networks for improved segmentation of renal histopathology. In: Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, pp. 151\u2013163 (2019)"},{"issue":"1","key":"25_CR5","first-page":"58","volume":"28","author":"N Chen","year":"2016","unstructured":"Chen, N., Zhou, Q.: The evolving gleason grading system. Chin. J. Cancer Res. 28(1), 58\u201364 (2016)","journal-title":"Chin. J. Cancer Res."},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 8789\u20138797 (2018)","DOI":"10.1109\/CVPR.2018.00916"},{"key":"25_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/978-3-030-00928-1_60","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"JP Cohen","year":"2018","unstructured":"Cohen, J.P., Luck, M., Honari, S.: Distribution matching losses can hallucinate features in medical image translation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 529\u2013536. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_60"},{"key":"25_CR8","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 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297\u2013302 (1945). https:\/\/www.jstor.org\/stable\/1932409","DOI":"10.2307\/1932409"},{"issue":"2","key":"25_CR10","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1111\/his.12008","volume":"62","author":"L Egevad","year":"2013","unstructured":"Egevad, L., et al.: Standardization of Gleason grading among 337 European pathologists. Histopathology 62(2), 247\u2013256 (2013)","journal-title":"Histopathology"},{"key":"25_CR11","doi-asserted-by":"crossref","unstructured":"Engin, D., Genc, A., Ekenel, H.K.: Cycle-dehaze: enhanced cyclegan for single image dehazing. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 938\u2013946 (2018)","DOI":"10.1109\/CVPRW.2018.00127"},{"key":"25_CR12","volume-title":"Digital Image Processing","author":"RC Gonzalez","year":"2007","unstructured":"Gonzalez, R.C., Woods, R.E., Masters, B.R.: Digital Image Processing, 3rd edn. Pearson, London (2007)","edition":"3"},{"key":"25_CR13","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. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6629\u20136640. NIPS\u201917 (2017)"},{"key":"25_CR14","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 5967\u20135976 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"issue":"4","key":"25_CR15","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1007\/s10278-017-9976-3","volume":"30","author":"MD Kohli","year":"2017","unstructured":"Kohli, M.D., Summers, R.M., Geis, J.R.: Medical image data and datasets in the era of machine learning - whitepaper from the 2016 c-mimi meeting dataset session. J. Digital Imag. 30(4), 392\u2013399 (2017)","journal-title":"J. Digital Imag."},{"key":"25_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1007\/978-3-030-59713-9_64","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"Y Ma","year":"2020","unstructured":"Ma, Y., et al.: Cycle structure and illumination constrained GAN for medical image enhancement. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 667\u2013677. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59713-9_64"},{"key":"25_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-33391-1_1","volume-title":"Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data","author":"I Manakov","year":"2019","unstructured":"Manakov, I., Rohm, M., Kern, C., Schworm, B., Kortuem, K., Tresp, V.: Noise as domain shift: denoising medical images by unpaired image translation. In: Wang, Q., et al. (eds.) DART\/MIL3ID -2019. LNCS, vol. 11795, pp. 3\u201310. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33391-1_1"},{"issue":"5","key":"25_CR18","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":"25_CR19","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 \u2014 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":"25_CR20","doi-asserted-by":"crossref","unstructured":"Shaban, M.T., Baur, C., Navab, N., Albarqouni, S.: Staingan: stain style transfer for digital histological images. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 953\u2013956 (2019)","DOI":"10.1109\/ISBI.2019.8759152"},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Siddiquee, M.M.R., et al.: Learning fixed points in generative adversarial networks: from image-to-image translation to disease detection and localization. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 191\u2013200 (2019)","DOI":"10.1109\/ICCV.2019.00028"},{"issue":"4","key":"25_CR22","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"25_CR23","unstructured":"Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multi-scale structural similarity for image quality assessment. In: The Thirty-Seventh Asilomar Conference on Signals, Systems and Computers 2003, pp. 1398\u20131402 (2003)"},{"key":"25_CR24","doi-asserted-by":"crossref","unstructured":"Zhu, J., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2242\u20132251 (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"25_CR25","doi-asserted-by":"crossref","unstructured":"Zimmermann, M., et al.: Deep learning-based molecular morphometrics for kidney biopsies. JCI Insight 6 (2021)","DOI":"10.1172\/jci.insight.144779"}],"container-title":["Lecture Notes in Computer Science","Medical Image Understanding and Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-80432-9_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T17:33:25Z","timestamp":1710264805000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-80432-9_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030804312","9783030804329"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-80432-9_25","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":"6 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIUA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual Conference on Medical Image Understanding and Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Oxford","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miua2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miua2021.com\/","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":"77","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":"32","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":"8","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":"42% - 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":"3,8","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":"3,3","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)"}}]}}