{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T06:47:43Z","timestamp":1767422863183,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030597214"},{"type":"electronic","value":"9783030597221"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-59722-1_30","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T17:03:01Z","timestamp":1601658181000},"page":"309-319","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Structure Preserving Stain Normalization of Histopathology Images Using Self Supervised Semantic Guidance"],"prefix":"10.1007","author":[{"given":"Dwarikanath","family":"Mahapatra","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Behzad","family":"Bozorgtabar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Philippe","family":"Thiran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling","family":"Shao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"30_CR1","unstructured":"Glas segmentation challenge results. https:\/\/warwick.ac.uk\/fac\/sci\/dcs\/research\/tia\/glascontest\/results\/. Accessed 30 Jan 2020"},{"key":"30_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1007\/978-3-030-32245-8_60","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"W Bai","year":"2019","unstructured":"Bai, W., et al.: Self-supervised learning for Cardiac MR image segmentation by anatomical position prediction. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 541\u2013549. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_60"},{"issue":"22","key":"30_CR3","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1001\/jama.2017.14585","volume":"318","author":"BE Bejnordi","year":"2017","unstructured":"Bejnordi, B.E., Veta, M., van Diest, P.J., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22), 2199\u20132210 (2017)","journal-title":"JAMA"},{"issue":"3","key":"30_CR4","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(3), 792\u2013802 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"30_CR5","doi-asserted-by":"crossref","unstructured":"Bozorgtabar, B., et al.: Informative sample generation using class aware generative adversarial networks for classification of chest Xrays. Comput. Vis. Image Underst. 184, 57\u201365 (2019)","DOI":"10.1016\/j.cviu.2019.04.007"},{"key":"30_CR6","doi-asserted-by":"crossref","unstructured":"B\u00e1ndi, P., et\u00a0al.: From detection of individual metastases to classification of lymph node status at the patient level: The CAMELYON17 challenge. IEEE Trans. Med. Imaging 38(2), 550\u2013560 (2019)","DOI":"10.1109\/TMI.2018.2867350"},{"key":"30_CR7","unstructured":"Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUs). In: Proceedings of ICLR (2016)"},{"key":"30_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/978-3-030-00934-2_19","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"M Gadermayr","year":"2018","unstructured":"Gadermayr, M., Appel, V., Klinkhammer, B.M., Boor, P., Merhof, D.: Which way round? a study on the performance of stain-translation for segmenting arbitrarily dyed histological images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 165\u2013173. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_19"},{"key":"30_CR9","unstructured":"Guizilini, V., Hou, R., Li, J., Ambrus, R., Gaidon, A.: Semantically-guided representation learning for self-supervised monocular depth. In: Proceedings of ICLR, pp. 1\u201314 (2020)"},{"key":"30_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1007\/978-3-030-32239-7_70","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"L Gupta","year":"2019","unstructured":"Gupta, L., Klinkhammer, B.M., Boor, P., Merhof, D., Gadermayr, M.: GAN-based image enrichment in digital pathology boosts segmentation accuracy. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 631\u2013639. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_70"},{"key":"30_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"30_CR12","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.compmedimag.2016.05.003","volume":"57","author":"A Janowczyk","year":"2017","unstructured":"Janowczyk, A., Basavanhally, A., Madabhushi, A.: Stain normalization using sparse autoencoders (STANOSA): application to digital pathology. Comput. Med. Imaging Graph 57, 50\u201361 (2017)","journal-title":"Comput. Med. Imaging Graph"},{"key":"30_CR13","unstructured":"Kazeminia, S., et al.: Gans for medical image analysis. In: arXiv preprint arXiv:1809.06222 (2018)"},{"issue":"6","key":"30_CR14","doi-asserted-by":"publisher","first-page":"1729","DOI":"10.1109\/TBME.2014.2303294","volume":"61","author":"A Khan","year":"2014","unstructured":"Khan, A., 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(6), 1729\u20131738 (2014)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"30_CR15","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: arXiv preprint arXiv:1412.6980 (2014)"},{"key":"30_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1007\/978-3-030-32239-7_63","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"A Lahiani","year":"2019","unstructured":"Lahiani, A., Navab, N., Albarqouni, S., Klaiman, E.: Perceptual embedding consistency for seamless reconstruction of Tilewise style transfer. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 568\u2013576. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_63"},{"key":"30_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"30_CR18","doi-asserted-by":"crossref","unstructured":"Macenko, M., et al.: A method for normalizing histology slides for quantitative analysis. In: IEEE International Symposium on Proceedings of Biomedical Imaging: From Nano to Macro, ISBI 2009, pp. 1107\u20131110 (2009)","DOI":"10.1109\/ISBI.2009.5193250"},{"key":"30_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1007\/978-3-319-66179-7_44","volume-title":"Medical Image Computing and Computer Assisted Intervention - MICCAI 2017","author":"D Mahapatra","year":"2017","unstructured":"Mahapatra, D., Bozorgtabar, B., Hewavitharanage, S., Garnavi, R.: Image super resolution using generative adversarial networks and local saliency maps for retinal image analysis. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 382\u2013390. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_44"},{"key":"30_CR20","doi-asserted-by":"crossref","unstructured":"Mahapatra, D., Bozorgtabar, B., Shao, L.: Pathological retinal region segmentation from oct images using geometric relation based augmentation. In: Proceedings of IEEE CVPR, pp. 9611\u20139620 (2020)","DOI":"10.1109\/CVPR42600.2020.00963"},{"key":"30_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1007\/978-3-030-00934-2_65","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"D Mahapatra","year":"2018","unstructured":"Mahapatra, D., Bozorgtabar, B., Thiran, J.-P., Reyes, M.: Efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 580\u2013588. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_65"},{"key":"30_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patcog.2019.107109","volume":"100","author":"D Mahapatra","year":"2020","unstructured":"Mahapatra, D., Ge, Z.: Training data independent image registration using generative adversarial networks and domain adaptation. Pattern Recogn. 100, 1\u201314 (2020)","journal-title":"Pattern Recogn."},{"issue":"5","key":"30_CR23","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/38.946629","volume":"21","author":"E Reinhard","year":"2001","unstructured":"Reinhard, E., Adhikhmin, 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":"30_CR24","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":"30_CR25","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1007\/s11548-018-1772-0","volume":"13","author":"T Ross","year":"2018","unstructured":"Ross, T., et al.: Gexploiting the potential of unlabeled endoscopic video data with self-supervised learning. Int. J. Comput. Assist. Radiol. Surg. 13, 925\u2013933 (2018)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"30_CR26","doi-asserted-by":"crossref","unstructured":"Shaban, M.T., Baur, C., Navab, N., Albarqouni, S.: StainGAN: stain style transfer for digital histological images. arXiv preprint arXiv:1804.01601 (2018)","DOI":"10.1109\/ISBI.2019.8759152"},{"key":"30_CR27","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.media.2016.08.008","volume":"35","author":"K Sirinukunwattana","year":"2017","unstructured":"Sirinukunwattana, K., et al.: Gland segmentation in colon histology images: the GlaS challenge contest. Med. Imaging Anal. 35, 489\u2013502 (2017)","journal-title":"Med. Imaging Anal."},{"key":"30_CR28","doi-asserted-by":"crossref","unstructured":"Su, H., Jampani, V., Sun, D., Gallo, O., Learned-Miller, E., Kautz, J.: Pixel-adaptive convolutional neural networks. In: Proceedings of IEEE CVPR, pp. 11166\u201311175 (2019)","DOI":"10.1109\/CVPR.2019.01142"},{"key":"30_CR29","doi-asserted-by":"crossref","unstructured":"Tajbakhsh, N., et al.: Surrogate supervision for medical image analysis: effective deep learning from limited quantities of labeled data. In: Proceedings of IEEE ISBI, pp. 1251\u20131255 (2019)","DOI":"10.1109\/ISBI.2019.8759553"},{"key":"30_CR30","doi-asserted-by":"crossref","unstructured":"Vahadane, A., et al.: Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans. Med. Imaging 35(8), 1962\u20131971 (2016)","DOI":"10.1109\/TMI.2016.2529665"},{"key":"30_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01261-8_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Wu","year":"2018","unstructured":"Wu, Y., He, K.: Group normalization. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01261-8_1"},{"key":"30_CR32","doi-asserted-by":"crossref","unstructured":"Yi, X., Walia, E., Babyn, P.: Generative adversarial network in medical imaging: a review. Med. Imaging Anal. 58, 101552 (2019)","DOI":"10.1016\/j.media.2019.101552"},{"key":"30_CR33","unstructured":"Zanjani, F.G., Zinger, S., Bejnordi, B.E., van der Laak, J.A.: Histopathology stain-color normalization using deep generative models. In: Proceedings of Medical Imaging with Deep Learning (2018)"},{"key":"30_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1007\/978-3-030-00937-3_82","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"M Zhao","year":"2018","unstructured":"Zhao, M., et al.: Craniomaxillofacial Bony structures segmentation from MRI with deep-supervision adversarial learning. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 720\u2013727. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_82"},{"key":"30_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-030-32239-7_77","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"N Zhou","year":"2019","unstructured":"Zhou, N., Cai, D., Han, X., Yao, J.: Enhanced cycle-consistent generative adversarial network for color normalization of H&E stained images. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 694\u2013702. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_77"},{"key":"30_CR36","doi-asserted-by":"crossref","unstructured":"Zhu, J., Park, T., Isola, P., Efros, A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: arXiv preprint arXiv:1703.10593 (2017)","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59722-1_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:08:30Z","timestamp":1759356510000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59722-1_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597214","9783030597221"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59722-1_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","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":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2020.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":"1809","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":"542","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":"30% - 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 due to the COVID-19 pandemic.","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)"}}]}}