{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T14:21:25Z","timestamp":1774362085674,"version":"3.50.1"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031438943","type":"print"},{"value":"9783031438950","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43895-0_49","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:23Z","timestamp":1696115303000},"page":"521-531","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["One-Shot Federated Learning on\u00a0Medical Data Using Knowledge Distillation with\u00a0Image Synthesis and\u00a0Client Model Adaptation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9165-870X","authenticated-orcid":false,"given":"Myeongkyun","family":"Kang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6995-2312","authenticated-orcid":false,"given":"Philip","family":"Chikontwe","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8937-6263","authenticated-orcid":false,"given":"Soopil","family":"Kim","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7885-4792","authenticated-orcid":false,"given":"Kyong Hwan","family":"Jin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0579-7763","authenticated-orcid":false,"given":"Ehsan","family":"Adeli","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5416-5159","authenticated-orcid":false,"given":"Kilian M.","family":"Pohl","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7476-1046","authenticated-orcid":false,"given":"Sang Hyun","family":"Park","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"49_CR1","first-page":"2556","volume":"34","author":"M Baradad Jurjo","year":"2021","unstructured":"Baradad Jurjo, M., Wulff, J., Wang, T., Isola, P., Torralba, A.: Learning to see by looking at noise. Adv. Neural. Inf. Process. Syst. 34, 2556\u20132569 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"49_CR2","doi-asserted-by":"crossref","unstructured":"Chen, H., et al.: Data-free learning of student networks. In: International Conference on Computer Vision, pp. 3514\u20133522 (2019)","DOI":"10.1109\/ICCV.2019.00361"},{"key":"49_CR3","doi-asserted-by":"publisher","unstructured":"Chikontwe, P., Nam, S.J., Go, H., Kim, M., Sung, H.J., Park, S.H.: Feature re-calibration based multiple instance learning for whole slide image classification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 420\u2013430. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-16434-7_41","DOI":"10.1007\/978-3-031-16434-7_41"},{"key":"49_CR4","doi-asserted-by":"crossref","unstructured":"Codella, N.C., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 168\u2013172. IEEE (2018)","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"49_CR5","unstructured":"Combalia, M., et al.: Bcn20000: dermoscopic lesions in the wild. arXiv preprint arXiv:1908.02288 (2019)"},{"key":"49_CR6","unstructured":"Dennis, D.K., Li, T., Smith, V.: Heterogeneity for the win: one-shot federated clustering. In: International Conference on Machine Learning, pp. 2611\u20132620. PMLR (2021)"},{"key":"49_CR7","unstructured":"EyePACS: Diabetic retinopathy detection (2015)"},{"key":"49_CR8","unstructured":"Guha, N., Talwalkar, A., Smith, V.: One-shot federated learning. arXiv preprint arXiv:1902.11175 (2019)"},{"key":"49_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"49_CR10","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"49_CR11","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456. PMLR (2015)"},{"key":"49_CR12","doi-asserted-by":"publisher","unstructured":"Jiang, M., Yang, H., Li, X., Liu, Q., Heng, P.A., Dou, Q.: Dynamic bank learning for semi-supervised federated image diagnosis with class imbalance. In: Medical Image Computing and Computer Assisted Intervention. pp. 196\u2013206. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-16437-8_19","DOI":"10.1007\/978-3-031-16437-8_19"},{"key":"49_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109061","volume":"133","author":"E Jung","year":"2023","unstructured":"Jung, E., Luna, M., Park, S.H.: Conditional gan with 3d discriminator for MRI generation of Alzheimer\u2019s disease progression. Pattern Recogn. 133, 109061 (2023)","journal-title":"Pattern Recogn."},{"key":"49_CR14","doi-asserted-by":"crossref","unstructured":"Kim, S., An, S., Chikontwe, P., Park, S.H.: Bidirectional rnn-based few shot learning for 3d medical image segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 1808\u20131816 (2021)","DOI":"10.1609\/aaai.v35i3.16275"},{"key":"49_CR15","doi-asserted-by":"crossref","unstructured":"Li, Q., He, B., Song, D.: Practical one-shot federated learning for cross-silo setting. In: International Joint Conference on Artificial Intelligence (2020)","DOI":"10.24963\/ijcai.2021\/205"},{"key":"49_CR16","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.patcog.2018.03.005","volume":"80","author":"Y Li","year":"2018","unstructured":"Li, Y., Wang, N., Shi, J., Liu, J., Hou, X.: Adaptive batch normalization for practical domain adaptation. Pattern Recogn. 80, 109\u2013117 (2018)","journal-title":"Pattern Recogn."},{"key":"49_CR17","first-page":"2351","volume":"33","author":"T Lin","year":"2020","unstructured":"Lin, T., Kong, L., Stich, S.U., Jaggi, M.: Ensemble distillation for robust model fusion in federated learning. Adv. Neural. Inf. Process. Syst. 33, 2351\u20132363 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"49_CR18","doi-asserted-by":"publisher","unstructured":"Liu, X., Li, W., Yuan, Y.: Intervention & interaction federated abnormality detection with noisy clients. In: Medical Image Computing and Computer Assisted Intervention, pp. 309\u2013319. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-16452-1_30","DOI":"10.1007\/978-3-031-16452-1_30"},{"key":"49_CR19","unstructured":"Liu, Y., Zhang, W., Wang, J., Wang, J.: Data-free knowledge transfer: a survey. arXiv preprint arXiv:2112.15278 (2021a"},{"key":"49_CR20","doi-asserted-by":"crossref","unstructured":"Mahendran, A., Vedaldi, A.: Understanding deep image representations by inverting them. In: Computer Vision and Pattern Recognition, pp. 5188\u20135196 (2015)","DOI":"10.1109\/CVPR.2015.7299155"},{"key":"49_CR21","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR (2017)"},{"key":"49_CR22","unstructured":"Micaelli, P., Storkey, A.J.: Zero-shot knowledge transfer via adversarial belief matching. In: Advances in Neural Information Processing Systems 32 (2019)"},{"key":"49_CR23","doi-asserted-by":"publisher","unstructured":"Qi, X., Yang, G., He, Y., Liu, W., Islam, A., Li, S.: Contrastive re-localization and history distillation in federated cmr segmentation. In: Medical Image Computing and Computer Assisted Intervention, pp. 256\u2013265. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-16443-9_25","DOI":"10.1007\/978-3-031-16443-9_25"},{"key":"49_CR24","unstructured":"Raikwar, P., Mishra, D.: Discovering and overcoming limitations of noise-engineered data-free knowledge distillation. In: Advances in Neural Information Processing Systems (2022)"},{"key":"49_CR25","unstructured":"RSNA: Rsna pneumonia detection challenge (2018)"},{"key":"49_CR26","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2014)"},{"key":"49_CR27","unstructured":"Ogier du Terrail, J., et al.: Datasets and benchmarks for cross-silo federated learning in realistic healthcare settings. In: Advances in Neural Information Processing Systems (2022)"},{"issue":"1","key":"49_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl, P., Rosendahl, C., Kittler, H.: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific Data 5(1), 1\u20139 (2018)","journal-title":"Scientific Data"},{"key":"49_CR29","unstructured":"Yang, J., et al.: Medmnist v2: A large-scale lightweight benchmark for 2d and 3d biomedical image classification. arXiv preprint arXiv:2110.14795 (2021)"},{"key":"49_CR30","doi-asserted-by":"crossref","unstructured":"Yin, H., et al.: Dreaming to distill: data-free knowledge transfer via deepinversion. In: Computer Vision and Pattern Recognition, pp. 8715\u20138724 (2020)","DOI":"10.1109\/CVPR42600.2020.00874"},{"key":"49_CR31","unstructured":"Yurochkin, M., Agarwal, M., Ghosh, S., Greenewald, K., Hoang, N., Khazaeni, Y.: Bayesian nonparametric federated learning of neural networks. In: International Conference on Machine Learning, pp. 7252\u20137261. PMLR (2019)"},{"key":"49_CR32","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. In: British Machine Vision Conference (BMVC) (2016)","DOI":"10.5244\/C.30.87"},{"key":"49_CR33","unstructured":"Zhang, J., et al.: Dense: data-free one-shot federated learning. In: Advances in Neural Information Processing Systems (2022)"},{"key":"49_CR34","first-page":"16001","volume":"33","author":"S Zhang","year":"2020","unstructured":"Zhang, S., Liu, M., Yan, J.: The diversified ensemble neural network. Adv. Neural. Inf. Process. Syst. 33, 16001\u201316011 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"49_CR35","unstructured":"Zhou, Y., Pu, G., Ma, X., Li, X., Wu, D.: Distilled one-shot federated learning. arXiv preprint arXiv:2009.07999 (2020)"},{"key":"49_CR36","doi-asserted-by":"publisher","unstructured":"Zhu, W., Luo, J.: Federated medical image analysis with virtual sample synthesis. In: Medical Image Computing and Computer Assisted Intervention, pp. 728\u2013738. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-16437-8_70","DOI":"10.1007\/978-3-031-16437-8_70"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43895-0_49","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T14:33:26Z","timestamp":1710167606000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43895-0_49"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031438943","9783031438950"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43895-0_49","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","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":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","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":"730","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":"32% - 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)"}}]}}