{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T15:25:54Z","timestamp":1772205954227,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164514","type":"print"},{"value":"9783031164521","type":"electronic"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16452-1_66","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T21:25:46Z","timestamp":1663277146000},"page":"695-704","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["FedHarmony: Unlearning Scanner Bias with\u00a0Distributed Data"],"prefix":"10.1007","author":[{"given":"Nicola K.","family":"Dinsdale","sequence":"first","affiliation":[]},{"given":"Mark","family":"Jenkinson","sequence":"additional","affiliation":[]},{"given":"Ana I. L.","family":"Namburete","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"66_CR1","doi-asserted-by":"publisher","unstructured":"Box, G.E.P., Cox, D.R.: An analysis of transformations. J. Roy. Stat. Soc. Ser. B (Methodol.) 26(2), 211\u2013252 (1964). https:\/\/doi.org\/10.2307\/2984418","DOI":"10.2307\/2984418"},{"key":"66_CR2","unstructured":"Consulting I: General Data Protection Regulation (GDPR), Sep 2019. https:\/\/gdpr-info.eu\/"},{"key":"66_CR3","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.mri.2019.05.041","volume":"64","author":"B Dewey","year":"2019","unstructured":"Dewey, B., et al.: DeepHarmony: a deep learning approach to contrast harmonization across scanner changes. Magn. Reson. Imaging 64, 160\u2013170 (2019)","journal-title":"Magn. Reson. Imaging"},{"key":"66_CR4","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1038\/mp.2013.78","volume":"19","author":"A Di Martino","year":"2013","unstructured":"Di Martino, A., et al.: The Autism brain imaging data exchange: towards large-scale evaluation of the intrinsic brain architecture in Autism. Mol. Psychiatry 19, 659\u2013667 (2013). https:\/\/doi.org\/10.1038\/mp.2013.78","journal-title":"Mol. Psychiatry"},{"key":"66_CR5","doi-asserted-by":"publisher","unstructured":"Dinsdale, N.K., Jenkinson, M., Namburete, A.I.: Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal. Neuroimage 228, 117689 (2021). https:\/\/doi.org\/10.1016\/j.neuroimage.2020.117689","DOI":"10.1016\/j.neuroimage.2020.117689"},{"key":"66_CR6","doi-asserted-by":"publisher","unstructured":"Dong, N., Voiculescu, I.: Federated contrastive learning for decentralized unlabeled medical images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 378\u2013387. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87199-4_36","DOI":"10.1007\/978-3-030-87199-4_36"},{"key":"66_CR7","doi-asserted-by":"crossref","unstructured":"Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) Automata, Languages and Programming, pp. 1\u201312 (2006)","DOI":"10.1007\/11787006_1"},{"key":"66_CR8","unstructured":"Feng, H., et al.: Kd3a: unsupervised multi-source decentralized domain adaptation via knowledge distillation. In: ICML (2021)"},{"key":"66_CR9","doi-asserted-by":"publisher","unstructured":"Han, X., et al.: Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. Neuroimage 32, 180\u201394 (2006). https:\/\/doi.org\/10.1016\/j.neuroimage.2006.02.051","DOI":"10.1016\/j.neuroimage.2006.02.051"},{"key":"66_CR10","doi-asserted-by":"publisher","unstructured":"Jovicich, J., et al.: Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data. Neuroimage 30, 436\u201343 (2006). https:\/\/doi.org\/10.1016\/j.neuroimage.2005.09.046","DOI":"10.1016\/j.neuroimage.2005.09.046"},{"key":"66_CR11","unstructured":"McMahan, H.B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: AISTATS (2017)"},{"key":"66_CR12","doi-asserted-by":"publisher","unstructured":"Moyer, D., Ver Steeg, G., Tax, C., Thompson, P.: Scanner invariant representations for diffusion MRI harmonization. Magn. Reson. Med. 84, 2174\u20132189 (2020). https:\/\/doi.org\/10.1002\/mrm.28243","DOI":"10.1002\/mrm.28243"},{"key":"66_CR13","unstructured":"Office for Civil Rights US: The hipaa privacy rule, Jul 2021. https:\/\/www.hhs.gov\/hipaa\/for-professionals\/privacy\/index.html"},{"key":"66_CR14","unstructured":"Peng, X., Huang, Z., Zhu, Y., Saenko, K.: Federated adversarial domain adaptation. ArXiv abs\/1911.02054 (2020)"},{"key":"66_CR15","unstructured":"Peterson, D., Kanani, P.H., Marathe, V.J.: Private federated learning with domain adaptation. ArXiv abs\/1912.06733 (2019)"},{"key":"66_CR16","unstructured":"Rasouli, M., Sun, T., Rajagopal, R.: Fedgan: Federated generative adversarialnetworks for distributed data. ArXiv, Jun 2020"},{"key":"66_CR17","unstructured":"Sahu, A., Li, T., Sanjabi, M., Zaheer, M., Talwalkar, A., Smith, V.: On the convergence of federated optimization in heterogeneous networks. ArXiv, Dec 2018"},{"key":"66_CR18","doi-asserted-by":"publisher","unstructured":"Sheller, M.J., Reina, G.A., Edwards, B., Martin, J., Bakas, S.: Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, pp. 92\u2013104 (2019). https:\/\/doi.org\/10.1007\/978-3-030-11723-8-9","DOI":"10.1007\/978-3-030-11723-8-9"},{"key":"66_CR19","doi-asserted-by":"publisher","unstructured":"Sweeney, L.: K-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(5), 557\u2013570 (2002). https:\/\/doi.org\/10.1142\/S0218488502001648","DOI":"10.1142\/S0218488502001648"},{"key":"66_CR20","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: Simultaneous deep transfer across domains and tasks. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.463"},{"key":"66_CR21","unstructured":"Yang, L., Beliard, C., Rossi, D.: Heterogeneous data-aware federated learning. ArXiv, Nov 2020"},{"key":"66_CR22","doi-asserted-by":"publisher","unstructured":"Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10, 1\u201319 (2019). https:\/\/doi.org\/10.1145\/3298981","DOI":"10.1145\/3298981"},{"key":"66_CR23","doi-asserted-by":"crossref","unstructured":"Yao, C.H., Gong, B., Cui, Y., Qi, H., Zhu, Y., Yang, M.H.: Federated multi-target domain adaptation. ArXiv (2021)","DOI":"10.1109\/WACV51458.2022.00115"},{"key":"66_CR24","doi-asserted-by":"publisher","unstructured":"Yu, M., et al.: Statistical harmonization corrects site effects in functional connectivity measurements from multisite fMRI data. Hum. Brain Mapp. 39, 4213\u20134227 (2018). https:\/\/doi.org\/10.1002\/hbm.24241","DOI":"10.1002\/hbm.24241"},{"key":"66_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, K., Jiang, Y., Seversky, L., Xu, C., Liu, D., Song, H.: Federated variational learning for anomaly detection in multivariate time series. ArXiv, Aug 2021","DOI":"10.1109\/IPCCC51483.2021.9679367"},{"key":"66_CR26","doi-asserted-by":"crossref","unstructured":"Zhao, F., Wu, Z., Wang, L., Lin, W., Xia, S., Li, G.: Harmonization of infant cortical thickness using surface-to-surface cycle-consistent adversarial networks. In: Medical Image Computing and Computer Assisted Intervention - Conference Proceedings, pp. 475\u2013483, Oct 2019","DOI":"10.1007\/978-3-030-32251-9_52"},{"key":"66_CR27","doi-asserted-by":"publisher","unstructured":"Zuo, L., et al.: Unsupervised mr harmonization by learning disentangled representations using information bottleneck theory. Neuroimage 243, 118569 (2021). https:\/\/doi.org\/10.1016\/j.neuroimage.2021.118569","DOI":"10.1016\/j.neuroimage.2021.118569"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16452-1_66","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T11:52:59Z","timestamp":1710244379000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16452-1_66"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164514","9783031164521"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16452-1_66","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","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":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","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 Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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":"5","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)"}}]}}