{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:08:58Z","timestamp":1775578138201,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030597092","type":"print"},{"value":"9783030597108","type":"electronic"}],"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\/978-3-030-59710-8_48","type":"book-chapter","created":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T18:06:41Z","timestamp":1601575601000},"page":"490-499","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Source-Relaxed Domain Adaptation for Image Segmentation"],"prefix":"10.1007","author":[{"given":"Mathilde","family":"Bateson","sequence":"first","affiliation":[]},{"given":"Hoel","family":"Kervadec","sequence":"additional","affiliation":[]},{"given":"Jose","family":"Dolz","sequence":"additional","affiliation":[]},{"given":"Herv\u00e9","family":"Lombaert","sequence":"additional","affiliation":[]},{"given":"Ismail","family":"Ben Ayed","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"48_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1007\/978-3-030-32245-8_37","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"M Bateson","year":"2019","unstructured":"Bateson, M., Kervadec, H., Dolz, J., Lombaert, H., Ayed, I.B.: Constrained domain adaptation for segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 326\u2013334. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_37"},{"key":"48_CR2","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., Van Gool, L.: Road: reality oriented adaptation for semantic segmentation of urban scenes. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00823"},{"key":"48_CR3","doi-asserted-by":"publisher","first-page":"99065","DOI":"10.1109\/ACCESS.2019.2929258","volume":"7","author":"Q Dou","year":"2019","unstructured":"Dou, Q., et al.: Pnp-adanet: plug-and-play adversarial domain adaptation network at unpaired cross-modality cardiac segmentation. IEEE Access 7, 99065\u201399076 (2019)","journal-title":"IEEE Access"},{"key":"48_CR4","doi-asserted-by":"crossref","unstructured":"Gholami, A., et al.: A novel domain adaptation framework for medical image segmentation. In: MICCAI Brainlesion Workshop (2018)","DOI":"10.1007\/978-3-030-11726-9_26"},{"key":"48_CR5","unstructured":"Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. In: NIPS (2004)"},{"key":"48_CR6","unstructured":"Hoffman, J., et al.: Cycada: cycle-consistent adversarial domain adaptation. In: ICML (2018)"},{"key":"48_CR7","doi-asserted-by":"crossref","unstructured":"Hong, W., Wang, Z., Yang, M., Yuan, J.: Conditional generative adversarial network for structured domain adaptation. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00145"},{"key":"48_CR8","unstructured":"Jabi, M., Pedersoli, M., Mitiche, A., Ben Ayed, I.: Deep clustering: On the link between discriminative models and k-means. IEEE TPAMI, 1 (2019)"},{"key":"48_CR9","doi-asserted-by":"crossref","unstructured":"Javanmardi, M., Tasdizen, T.: Domain adaptation for biomedical image segmentation using adversarial training. In: ISBI (2018)","DOI":"10.1109\/ISBI.2018.8363637"},{"key":"48_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1007\/978-3-319-59050-9_47","volume-title":"Information Processing in Medical Imaging","author":"K Kamnitsas","year":"2017","unstructured":"Kamnitsas, K., et al.: Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 597\u2013609. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_47"},{"key":"48_CR11","first-page":"88","volume":"54","author":"H Kervadec","year":"2019","unstructured":"Kervadec, H., Dolz, J., Tang, M., Granger, E., Boykov, Y., Ayed, I.B.: Constrained-CNN losses for weakly supervised segmentation. MedIA 54, 88\u201399 (2019)","journal-title":"MedIA"},{"key":"48_CR12","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2014)"},{"key":"48_CR13","unstructured":"Krause, A., Perona, P., Gomes, R.G.: Discriminative clustering by regularized information maximization. In: NIPS (2010)"},{"key":"48_CR14","first-page":"60","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. MedIA 42, 60\u201388 (2017)","journal-title":"MedIA"},{"key":"48_CR15","unstructured":"Morerio, P., Cavazza, J., Murino, V.: Minimal-entropy correlation alignment for unsupervised deep domain adaptation. In: ICLR (2018)"},{"key":"48_CR16","unstructured":"Paszke, A., Chaurasia, A., Kim, S., Culurciello, E.: Enet: a deep neural network architecture for real-time semantic segmentation. arXiv 1606.02147 (2016)"},{"key":"48_CR17","doi-asserted-by":"crossref","unstructured":"Tsai, Y., Hung, W., Schulter, S., Sohn, K., Yang, M., Chandraker, M.: Learning to adapt structured output space for semantic segmentation. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00780"},{"key":"48_CR18","doi-asserted-by":"crossref","unstructured":"Tzeng, E., et al.: Adversarial discriminative domain adaptation. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.316"},{"key":"48_CR19","doi-asserted-by":"crossref","unstructured":"Vu, T.H., Jain, H., Bucher, M., Cord, M., P\u00e9rez, P.: Advent: adversarial entropy minimization for domain adaptation in semantic segmentation. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00262"},{"key":"48_CR20","unstructured":"Wu, X., Zhang, S., Zhou, Q., Yang, Z., Zhao, C., Latecki, L.J.: Entropy minimization vs. diversity maximization for domain adaptation. arXiv 2002.01690 (2020)"},{"key":"48_CR21","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Dollar, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.634"},{"key":"48_CR22","doi-asserted-by":"publisher","first-page":"1823","DOI":"10.1109\/TPAMI.2019.2903401","volume":"42","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., David, P., Foroosh, H., Gong, B.: A curriculum domain adaptation approach to the semantic segmentation of urban scenes. IEEE TPAMI 42, 1823\u20131841 (2019)","journal-title":"IEEE TPAMI"},{"key":"48_CR23","first-page":"46","volume":"38","author":"H Zhao","year":"2019","unstructured":"Zhao, H., et al.: Supervised segmentation of un-annotated retinal fundus images by synthesis. IEEE TMI 38, 46\u201356 (2019)","journal-title":"IEEE TMI"},{"key":"48_CR24","doi-asserted-by":"crossref","unstructured":"Zhou, Y., et al.: Prior-aware neural network for partially-supervised multi-organ segmentation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.01077"},{"key":"48_CR25","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., 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) (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"48_CR26","doi-asserted-by":"crossref","unstructured":"Zou, Y., Yu, Z., Kumar, B.V.K.V., Wang, J.: Unsupervised domain adaptation for semantic segmentation via class-balanced self-training. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01219-9_18"}],"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\/978-3-030-59710-8_48","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:03:35Z","timestamp":1759356215000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59710-8_48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597092","9783030597108"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59710-8_48","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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)"}}]}}