{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T23:53:41Z","timestamp":1770335621473,"version":"3.49.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030877217","type":"print"},{"value":"9783030877224","type":"electronic"}],"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\/978-3-030-87722-4_4","type":"book-chapter","created":{"date-parts":[[2021,9,24]],"date-time":"2021-09-24T01:17:40Z","timestamp":1632446260000},"page":"35-45","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Adversarial Continual Learning for Multi-domain Hippocampal Segmentation"],"prefix":"10.1007","author":[{"given":"Marius","family":"Memmel","sequence":"first","affiliation":[]},{"given":"Camila","family":"Gonzalez","sequence":"additional","affiliation":[]},{"given":"Anirban","family":"Mukhopadhyay","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"4_CR1","doi-asserted-by":"crossref","unstructured":"Alharbi, Y., Smith, N., Wonka, P.: Latent filter scaling for multimodal unsupervised image-to-image translation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019","DOI":"10.1109\/CVPR.2019.00155"},{"key":"4_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1007\/978-3-030-01219-9_9","volume-title":"Computer Vision \u2013 ECCV 2018","author":"R Aljundi","year":"2018","unstructured":"Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: learning what (not) to forget. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 144\u2013161. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01219-9_9"},{"key":"4_CR3","unstructured":"Baweja, C., Glocker, B., Kamnitsas, K.: Towards continual learning in medical imaging. CoRR abs\/1811.02496 (2018)"},{"key":"4_CR4","doi-asserted-by":"crossref","unstructured":"Boccardi, M., et al.: Training labels for hippocampal segmentation based on the EADC-ADNI harmonized hippocampal protocol. Alzheimer\u015b Dementia 11(2), 175\u2013183 (2015). http:\/\/adni.loni.usc.edu\/","DOI":"10.1016\/j.jalz.2014.12.002"},{"key":"4_CR5","doi-asserted-by":"publisher","first-page":"101535","DOI":"10.1016\/j.media.2019.101535","volume":"58","author":"A Chartsias","year":"2019","unstructured":"Chartsias, A., et al.: Disentangled representation learning in cardiac image analysis. Med. Image Anal. 58, 101535 (2019)","journal-title":"Med. Image Anal."},{"key":"4_CR6","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, NIPS 2016, pp. 2180\u20132188. Curran Associates Inc., Red Hook (2016)"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Douillard, A., Chen, Y., Dapogny, A., Cord, M.: PLOP: learning without forgetting for continual semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4040\u20134050, June 2021","DOI":"10.1109\/CVPR46437.2021.00403"},{"key":"4_CR8","unstructured":"van Garderen, K.A., Voort, S.V.D., Incekara, F., Smits, M., Klein, S.: Towards continuous learning for glioma segmentation with elastic weight consolidation. ArXiv abs\/1909.11479 (2019)"},{"key":"4_CR9","unstructured":"Gonz\u00e1lez, C., Sakas, G., Mukhopadhyay, A.: What is wrong with continual learning in medical image segmentation? CoRR abs\/2010.11008 (2020)"},{"key":"4_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1007\/978-3-030-59713-9_35","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"J Hofmanninger","year":"2020","unstructured":"Hofmanninger, J., Perkonigg, M., Brink, J.A., Pianykh, O., Herold, C., Langs, G.: Dynamic memory to alleviate catastrophic forgetting in continuous learning settings. In: Martel, A.L. (ed.) MICCAI 2020. LNCS, vol. 12262, pp. 359\u2013368. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59713-9_35"},{"key":"4_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/978-3-030-01219-9_11","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Huang","year":"2018","unstructured":"Huang, X., Liu, M.-Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 179\u2013196. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01219-9_11"},{"key":"4_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/978-3-030-59713-9_34","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"J Jiang","year":"2020","unstructured":"Jiang, J., Veeraraghavan, H.: Unified cross-modality feature Disentangler for unsupervised multi-domain MRI abdomen organs segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 347\u2013358. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59713-9_34"},{"key":"4_CR13","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":"4_CR14","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019","DOI":"10.1109\/CVPR.2019.00453"},{"key":"4_CR15","unstructured":"Kazeminia, S., et al.: GANs for medical image analysis. CoRR abs\/1809.06222 (2018)"},{"key":"4_CR16","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. In: Bengio, Y., LeCun, Y. (eds.) ICLR (2014)"},{"issue":"13","key":"4_CR17","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1073\/pnas.1611835114","volume":"114","author":"J Kirkpatrick","year":"2017","unstructured":"Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. Proc. Natl. Acad. Sci. 114(13), 3521\u20133526 (2017)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"4_CR18","doi-asserted-by":"publisher","unstructured":"Kulaga-Yoskovitz, J., et al.: Multi-contrast submillimetric 3 tesla hippocampal subfield segmentation protocol and dataset. Sci. Data 2(1), 150059 (2015). https:\/\/doi.org\/10.5061\/dryad.gc72v. https:\/\/datadryad.org\/stash\/dataset\/","DOI":"10.5061\/dryad.gc72v"},{"key":"4_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1007\/978-3-030-01246-5_3","volume-title":"Computer Vision \u2013 ECCV 2018","author":"H-Y Lee","year":"2018","unstructured":"Lee, H.-Y., Tseng, H.-Y., Huang, J.-B., Singh, M., Yang, M.-H.: Diverse image-to-image translation via disentangled representations. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 36\u201352. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01246-5_3"},{"key":"4_CR20","unstructured":"Lenga, M., Schulz, H., Saalbach, A.: Continual learning for domain adaptation in chest x-ray classification. In: Arbel, T., Ben Ayed, I., de Bruijne, M., Descoteaux, M., Lombaert, H., Pal, C. (eds.) Proceedings of the Third Conference on Medical Imaging with Deep Learning. Proceedings of Machine Learning Research, vol. 121, pp. 413\u2013423. PMLR, 06\u201308 July 2020"},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"Li, H., et al.: Denoising scanner effects from multimodal MRI data using linked independent component analysis. NeuroImage 208, 116388 (2020)","DOI":"10.1016\/j.neuroimage.2019.116388"},{"key":"4_CR22","doi-asserted-by":"crossref","unstructured":"Liu, Y.C., Yeh, Y.Y., Fu, T.C., Wang, S.D., Chiu, W.C., Wang, Y.C.F.: Detach and adapt: learning cross-domain disentangled deep representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018","DOI":"10.1109\/CVPR.2018.00924"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Michieli, U., Zanuttigh, P.: Incremental learning techniques for semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops, October 2019","DOI":"10.1109\/ICCVW.2019.00400"},{"key":"4_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1007\/978-3-030-59861-7_43","volume-title":"Machine Learning in Medical Imaging","author":"S \u00d6zg\u00fcn","year":"2020","unstructured":"\u00d6zg\u00fcn, S., Rickmann, A.-M., Roy, A.G., Wachinger, C.: Importance driven continual learning for segmentation across domains. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds.) MLMI 2020. LNCS, vol. 12436, pp. 423\u2013433. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59861-7_43"},{"key":"4_CR25","doi-asserted-by":"crossref","unstructured":"Pianykh, O.S., et al.: Continuous learning AI in radiology: implementation principles and early applications. Radiology 297(1), 6\u201314 (2020). pMID: 32840473","DOI":"10.1148\/radiol.2020200038"},{"key":"4_CR26","doi-asserted-by":"crossref","unstructured":"Prangemeier, T., Wildner, C., Fran\u00e7ani, A.O., Reich, C., Koeppl, H.: Multiclass yeast segmentation in microstructured environments with deep learning. In: 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 1\u20138 (2020)","DOI":"10.1109\/CIBCB48159.2020.9277693"},{"key":"4_CR27","unstructured":"Simpson, A.L., et al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. CoRR abs\/1902.09063 (2019). http:\/\/medicaldecathlon.com\/"},{"key":"4_CR28","unstructured":"Sokar, G., Mocanu, D.C., Pechenizkiy, M.: Learning invariant representation for continual learning. CoRR abs\/2101.06162 (2021)"},{"key":"4_CR29","unstructured":"Yu, X., Ying, Z., Li, G.: Multi-mapping image-to-image translation with central biasing normalization. CoRR abs\/1806.10050 (2018)"}],"container-title":["Lecture Notes in Computer Science","Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87722-4_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T08:20:06Z","timestamp":1680769206000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87722-4_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030877217","9783030877224"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87722-4_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DART","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Domain Adaptation and Representation Transfer","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":"1 October 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":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dart2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/dart2021\/home","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":"21","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":"13","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":"62% - 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":"3.05","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":"2","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":"No","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)"}}]}}