{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T05:54:25Z","timestamp":1780725265209,"version":"3.54.1"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031474002","type":"print"},{"value":"9783031474019","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\/978-3-031-47401-9_32","type":"book-chapter","created":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T11:02:29Z","timestamp":1701342149000},"page":"334-346","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Federated Model Aggregation via\u00a0Self-supervised Priors for\u00a0Highly Imbalanced Medical Image Classification"],"prefix":"10.1007","author":[{"given":"Marawan","family":"Elbatel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hualiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Mart","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huazhu","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaomeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,12,1]]},"reference":[{"key":"32_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-031-16852-9_1","volume-title":"Domain Adaptation and Representation Transfer","author":"PJ Bevan","year":"2022","unstructured":"Bevan, P.J., Atapour-Abarghouei, A.: Detecting melanoma fairly: skin tone detection and debiasing for skin lesion classification. In: Kamnitsas, K., et al. (eds.) Domain Adaptation and Representation Transfer, pp. 1\u201311. Springer Nature Switzerland, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16852-9_1"},{"issue":"1","key":"32_CR2","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1038\/s41597-020-00622-y","volume":"7","author":"H Borgli","year":"2019","unstructured":"Borgli, H., et al.: Hyperkvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy. Sci. Data 7(1), 283 (2019)","journal-title":"Sci. Data"},{"key":"32_CR3","unstructured":"Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. In: NeurIPS (2019)"},{"key":"32_CR4","unstructured":"Chen, H.Y., Tu, C.H., Li, Z., Shen, H.W., Chao, W.L.: On the importance and applicability of pre-training for federated learning. In: ICLR (2023)"},{"key":"32_CR5","unstructured":"Chen, Z., Liu, S., Wang, H., Yang, H.H., Quek, T.Q.S., Liu, Z.: Towards federated long-tailed learning. ArXiv abs\/2206.14988 (2022)"},{"key":"32_CR6","doi-asserted-by":"publisher","first-page":"2925","DOI":"10.1245\/s10434-011-1706-3","volume":"18","author":"KK Collins","year":"2011","unstructured":"Collins, K.K., Fields, R.C., Baptiste, D.F., Liu, Y., Moley, J.F., Jeffe, D.B.: Racial differences in survival after surgical treatment for melanoma. Ann. Surg. Oncol. 18, 2925\u20132936 (2011)","journal-title":"Ann. Surg. Oncol."},{"key":"32_CR7","unstructured":"Combalia, M., et al.: Bcn20000: Dermoscopic lesions in the wild. ArXiv abs\/1908.02288 (2019)"},{"key":"32_CR8","doi-asserted-by":"publisher","unstructured":"Ding, X., Liu, Z., Li, X.: Free lunch for surgical video understanding by distilling self-supervisions. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13437, pp. 365\u2013375. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16449-1_35","DOI":"10.1007\/978-3-031-16449-1_35"},{"key":"32_CR9","doi-asserted-by":"crossref","unstructured":"Elbatel, M., Mart\u00ed, R., Li, X.: FoPro-KD: fourier prompted effective knowledge distillation for long-tailed medical image recognition. ArXiv abs\/2305.17421 (2023)","DOI":"10.1109\/TMI.2023.3327428"},{"key":"32_CR10","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1007\/978-3-030-87240-3_31","volume-title":"Medical Image Computing and Computer Assisted Intervention - MICCAI 2021","author":"A Galdran","year":"2021","unstructured":"Galdran, A., Carneiro, G., Gonz\u00e1lez Ballester, M.A.: Balanced-mixup for highly imbalanced medical image classification. In: de Bruijne, M., et al. (eds.) Medical Image Computing and Computer Assisted Intervention - MICCAI 2021, pp. 323\u2013333. Springer International Publishing, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87240-3_31"},{"key":"32_CR11","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., et al.: GradViT: gradient inversion of vision transformers. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10011\u201310020 (2022)","DOI":"10.1109\/CVPR52688.2022.00978"},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.B.: Momentum contrast for unsupervised visual representation learning. In: CVPR, pp. 9726\u20139735 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"32_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"32_CR14","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1007\/978-3-031-17027-0_3","volume-title":"Data Augmentation, Labelling, and Imperfections","author":"G Holste","year":"2022","unstructured":"Holste, G., Wang, S., Jiang, Z., Shen, T.C., Shih, G., Summers, R.M., Peng, Y., Wang, Z.: Long-tailed classification of thorax diseases on chest x-ray: a new benchmark study. In: Nguyen, H.V., Huang, S.X., Xue, Y. (eds.) Data Augmentation, Labelling, and Imperfections, pp. 22\u201332. Springer Nature Switzerland, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-17027-0_3"},{"key":"32_CR15","unstructured":"Jee Cho, Y., Wang, J., Joshi, G.: Towards understanding biased client selection in federated learning. In: Camps-Valls, G., Ruiz, F.J.R., Valera, I. (eds.) Proceedings of The 25th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 151, pp. 10351\u201310375. PMLR, 28\u201330 March 2022"},{"key":"32_CR16","doi-asserted-by":"crossref","unstructured":"Jiang, M., et al.: Fair federated medical image segmentation via client contribution estimation. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.01564"},{"key":"32_CR17","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1007\/978-3-031-16437-8_44","volume-title":"MICCAI 2022","author":"L Ju","year":"2022","unstructured":"Ju, L., et al.: Flexible sampling for long-tailed skin lesion classification. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022, pp. 462\u2013471. Springer Nature Switzerland, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16437-8_44"},{"key":"32_CR18","unstructured":"Kang, B., et al.: Decoupling representation and classifier for long-tailed recognition. In: ICLR (2020)"},{"key":"32_CR19","doi-asserted-by":"crossref","unstructured":"Li, Q., He, B., Song, D.: Model-contrastive federated learning. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"32_CR20","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. In: Dhillon, I., Papailiopoulos, D., Sze, V. (eds.) Proceedings of Machine Learning and Systems, vol. 2, pp. 429\u2013450 (2020)"},{"key":"32_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/978-3-030-87199-4_31","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Q Liu","year":"2021","unstructured":"Liu, Q., Yang, H., Dou, Q., Heng, P.-A.: Federated semi-supervised medical image classification via inter-client relation matching. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 325\u2013335. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87199-4_31"},{"key":"32_CR22","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. In: ICLR (2017)"},{"key":"32_CR23","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":"32_CR24","doi-asserted-by":"crossref","unstructured":"Mendieta, M., Yang, T., Wang, P., et al.: Local learning matters: rethinking data heterogeneity in federated learning. In: CVPR, pp. 8397\u20138406 (2022)","DOI":"10.1109\/CVPR52688.2022.00821"},{"key":"32_CR25","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.future.2023.01.019","volume":"143","author":"X Mu","year":"2021","unstructured":"Mu, X., et al.: FedProc: prototypical contrastive federated learning on non-IID data. Future Gener. Comput. Syst. 143, 93\u2013104 (2021)","journal-title":"Future Gener. Comput. Syst."},{"key":"32_CR26","unstructured":"Nguyen, J., Wang, J., Malik, K., Sanjabi, M., Rabbat, M.: Where to begin? on the impact of pre-training and initialization in federated learning. In: ICLR (2023)"},{"key":"32_CR27","unstructured":"Reinke, A., Christodoulou, E., Glocker, B., et al.: Metrics reloaded - a new recommendation framework for biomedical image analysis validation. In: Medical Imaging with Deep Learning (2022)"},{"key":"32_CR28","unstructured":"Ren, J., et al.: Balanced meta-softmax for long-tailed visual recognition. In: Proceedings of Neural Information Processing Systems (NeurIPS), December 2020"},{"key":"32_CR29","doi-asserted-by":"crossref","unstructured":"Shang, X., Lu, Y., Huang, G., Wang, H.: Federated learning on heterogeneous and long-tailed data via classifier re-training with federated features. In: Raedt, L.D. (ed.) IJCAI, pp. 2218\u20132224, July 2022","DOI":"10.24963\/ijcai.2022\/308"},{"key":"32_CR30","doi-asserted-by":"crossref","unstructured":"Tang, K., Tao, M., Qi, J., Liu, Z., Zhang, H.: Invariant feature learning for generalized long-tailed classification. In: ECCV, p. 709\u2013726 (2022)","DOI":"10.1007\/978-3-031-20053-3_41"},{"key":"32_CR31","unstructured":"Ogier du Terrail, J., Ayed, S.S., et al.: FLamby: datasets and benchmarks for cross-silo federated learning in realistic healthcare settings. In: NeurIPS. vol. 35, pp. 5315\u20135334, Curran Associates, Inc. (2022)"},{"key":"32_CR32","unstructured":"Wicaksana, J., Yan, Z., Cheng, K.T.: FCA: taming long-tailed federated medical image classification by classifier anchoring. ArXiv abs\/2305.00738 (2023)"},{"key":"32_CR33","unstructured":"Zhang, J., Li, Z., et al.: Federated learning with label distribution skew via logits calibration, vol. 162, pp. 26311\u201326329. Proceedings of Machine Learning Research, 17\u201323 July 2022"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-47401-9_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T11:07:47Z","timestamp":1701342467000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47401-9_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031474002","9783031474019"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47401-9_32","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 December 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)"}}]}}