{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T16:03:18Z","timestamp":1780761798685,"version":"3.54.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030908737","type":"print"},{"value":"9783030908744","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\/978-3-030-90874-4_10","type":"book-chapter","created":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T05:03:05Z","timestamp":1636779785000},"page":"101-110","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Multi-task Federated Learning for Heterogeneous Pancreas Segmentation"],"prefix":"10.1007","author":[{"given":"Chen","family":"Shen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pochuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Holger R.","family":"Roth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daguang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masahiro","family":"Oda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weichung","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chiou-Shann","family":"Fuh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Po-Ting","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kao-Lang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-Chih","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kensaku","family":"Mori","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,11,14]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.ejmp.2020.03.011","volume":"72","author":"E Czeizler","year":"2020","unstructured":"Czeizler, E., et al.: Using federated data sources and varian learning portal framework to train a neural network model for automatic organ segmentation. Physica Med. 72, 39\u201345 (2020)","journal-title":"Physica Med."},{"issue":"1","key":"10_CR2","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1038\/s41746-021-00431-6","volume":"4","author":"Q Dou","year":"2021","unstructured":"Dou, Q., et al.: Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study. Npj Digit. Med. 4(1), 60 (2021). https:\/\/doi.org\/10.1038\/s41746-021-00431-6","journal-title":"Npj Digit. Med."},{"key":"10_CR3","doi-asserted-by":"publisher","unstructured":"Flores, M., et al.: Federated learning used for predicting outcomes in SARS-COV-2 patients (2021). https:\/\/doi.org\/10.21203\/rs.3.rs-126892\/v1","DOI":"10.21203\/rs.3.rs-126892\/v1"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Guo, M., Haque, A., Huang, D.A., Yeung, S., Fei-Fei, L.: Dynamic task prioritization for multitask learning. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 270\u2013287 (2018)","DOI":"10.1007\/978-3-030-01270-0_17"},{"key":"10_CR5","doi-asserted-by":"publisher","unstructured":"Landmanm, B., et al.: 2015 MICCAI multi-atlas labeling beyond the cranial vault - workshop and challenge (2015). https:\/\/doi.org\/10.7303\/syn3193805","DOI":"10.7303\/syn3193805"},{"key":"10_CR6","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. arXiv preprint arXiv:1812.06127 (2018)"},{"key":"10_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/978-3-030-32692-0_16","volume-title":"Machine Learning in Medical Imaging","author":"W Li","year":"2019","unstructured":"Li, W., et al.: Privacy-preserving federated brain tumour segmentation. In: Suk, H.-I., Liu, M., Yan, P., Lian, C. (eds.) MLMI 2019. LNCS, vol. 11861, pp. 133\u2013141. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32692-0_16"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Liu, S., Johns, E., Davison, A.J.: End-to-end multi-task learning with attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00197"},{"issue":"8","key":"10_CR9","doi-asserted-by":"publisher","first-page":"1971","DOI":"10.1109\/TMI.2019.2911588","volume":"38","author":"Y Man","year":"2019","unstructured":"Man, Y., Huang, Y., Feng, J., Li, X., Wu, F.: Deep Q learning driven CT pancreas segmentation with geometry-aware U-net. IEEE Trans. Med. Imaging 38(8), 1971\u20131980 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10_CR10","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":"10_CR11","unstructured":"Oktay, O., et al.: Attention U-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"},{"key":"10_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1007\/978-3-319-24553-9_68","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"HR Roth","year":"2015","unstructured":"Roth, H.R., et al.: DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 556\u2013564. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24553-9_68"},{"key":"10_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1007\/978-3-030-11723-8_9","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"MJ Sheller","year":"2019","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: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11383, pp. 92\u2013104. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11723-8_9"},{"key":"10_CR14","unstructured":"Simpson, A.L., et al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063 (2019)"},{"key":"10_CR15","unstructured":"Smith, V., Chiang, C.K., Sanjabi, M., Talwalkar, A.: Federated multi-task learning. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 4427\u20134437 (2017)"},{"key":"10_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1007\/978-3-030-60548-3_19","volume-title":"Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning","author":"P Wang","year":"2020","unstructured":"Wang, P., et al.: Automated pancreas segmentation using multi-institutional collaborative deep learning. In: Albarqouni, S. (ed.) DART\/DCL -2020. LNCS, vol. 12444, pp. 192\u2013200. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-60548-3_19"},{"key":"10_CR17","unstructured":"Xia, Y., et al.: Auto-FedAvg: learnable federated averaging for multi-institutional medical image segmentation (2021)"},{"key":"10_CR18","doi-asserted-by":"publisher","first-page":"101992","DOI":"10.1016\/j.media.2021.101992","volume":"70","author":"D Yang","year":"2021","unstructured":"Yang, D., et al.: Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from china, Italy, Japan. Med. Image Anal. 70, 101992 (2021)","journal-title":"Med. Image Anal."},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Yu, Q., et al.: C2FNAS: coarse-to-Fine neural architecture search for 3D medical image segmentation, December 2019","DOI":"10.1109\/CVPR42600.2020.00418"},{"key":"10_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1007\/978-3-319-66182-7_79","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"Y Zhou","year":"2017","unstructured":"Zhou, Y., Xie, L., Shen, W., Wang, Y., Fishman, E.K., Yuille, A.L.: A fixed-point model for pancreas segmentation in abdominal CT scans. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 693\u2013701. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_79"}],"container-title":["Lecture Notes in Computer Science","Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-90874-4_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T05:04:02Z","timestamp":1636779842000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-90874-4_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030908737","9783030908744"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-90874-4_10","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":"14 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DCL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Distributed and Collaborative Learning","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":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dcl2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dcl-workshop.github.io\/","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":"7","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":"4","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":"57% - 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":"2.5","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":"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 workshop 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)"}}]}}