{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:25:16Z","timestamp":1743092716184,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031450860"},{"type":"electronic","value":"9783031450877"}],"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\/978-3-031-45087-7_5","type":"book-chapter","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T19:01:37Z","timestamp":1696705297000},"page":"42-50","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Federated Multi-organ Dynamic Attention Segmentation Network with\u00a0Small CT Dataset"],"prefix":"10.1007","author":[{"given":"Li","family":"Li","sequence":"first","affiliation":[]},{"given":"Yunxin","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Youjian","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zezhou","family":"Li","sequence":"additional","affiliation":[]},{"given":"Guanqun","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Haotian","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Zhicheng","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,8]]},"reference":[{"key":"5_CR1","unstructured":"MICCAI FLARE22 challenge dataset (2022). https:\/\/zenodo.org\/record\/7860267#.ZFm_oXZBxrq"},{"key":"5_CR2","unstructured":"Multi-modality abdominal multi-organ segmentation challenge 2022 dataset (2022). https:\/\/amos22.grand-challenge.org\/"},{"key":"5_CR3","unstructured":"Synapse dataset (2023). https:\/\/www.synapse.org\/#!Synapse:syn3193805\/wiki\/89480"},{"key":"5_CR4","unstructured":"TCIA dataset (2023). https:\/\/wiki.cancerimagingarchive.net\/display\/Public\/Pancreas-CT"},{"key":"5_CR5","unstructured":"Chen, J., et al.: TransUNet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"5_CR6","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.ejmp.2021.05.003","volume":"85","author":"Y Fu","year":"2021","unstructured":"Fu, Y., Lei, Y., Wang, T., Curran, W.J., Liu, T., Yang, X.: A review of deep learning based methods for medical image multi-organ segmentation. Physica Med. 85, 107\u2013122 (2021)","journal-title":"Physica Med."},{"issue":"1","key":"5_CR7","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1146\/annurev.bioeng.1.1.211","volume":"1","author":"RD Howe","year":"1999","unstructured":"Howe, R.D., Matsuoka, Y.: Robotics for surgery. Annu. Rev. Biomed. Eng. 1(1), 211\u2013240 (1999)","journal-title":"Annu. Rev. Biomed. Eng."},{"issue":"8","key":"5_CR8","doi-asserted-by":"publisher","first-page":"866","DOI":"10.2174\/1574893615999200425232601","volume":"15","author":"Q Li","year":"2020","unstructured":"Li, Q., Song, H., Chen, L., Meng, X., Yang, J., Zhang, L.: An overview of abdominal multi-organ segmentation. Curr. Bioinform. 15(8), 866\u2013877 (2020)","journal-title":"Curr. Bioinform."},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., Yang, J.: Selective kernel networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 510\u2013519 (2019)","DOI":"10.1109\/CVPR.2019.00060"},{"key":"5_CR10","doi-asserted-by":"publisher","first-page":"102156","DOI":"10.1016\/j.media.2021.102156","volume":"73","author":"X Liang","year":"2021","unstructured":"Liang, X., Li, N., Zhang, Z., Xiong, J., Zhou, S., Xie, Y.: Incorporating the hybrid deformable model for improving the performance of abdominal CT segmentation via multi-scale feature fusion network. Med. Image Anal. 73, 102156 (2021)","journal-title":"Med. Image Anal."},{"key":"5_CR11","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":"5_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"3","key":"5_CR13","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1148\/radiol.11091710","volume":"261","author":"B Van Ginneken","year":"2011","unstructured":"Van Ginneken, B., Schaefer-Prokop, C.M., Prokop, M.: Computer-aided diagnosis: how to move from the laboratory to the clinic. Radiology 261(3), 719\u2013732 (2011)","journal-title":"Radiology"},{"key":"5_CR14","doi-asserted-by":"crossref","unstructured":"Wang, H., Cao, P., Wang, J., Zaiane, O.R.: Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 3, pp. 2441\u20132449 (2022)","DOI":"10.1609\/aaai.v36i3.20144"},{"key":"5_CR15","doi-asserted-by":"publisher","first-page":"104791","DOI":"10.1016\/j.bspc.2023.104791","volume":"84","author":"H Xiao","year":"2023","unstructured":"Xiao, H., Li, L., Liu, Q., Zhu, X., Zhang, Q.: Transformers in medical image segmentation: a review. Biomed. Signal Process. Control 84, 104791 (2023)","journal-title":"Biomed. Signal Process. Control"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Yao, C., Hu, M., Li, Q., Zhai, G., Zhang, X.P.: TransClaw U-Net: claw U-Net with transformers for medical image segmentation. In: 2022 5th International Conference on Information Communication and Signal Processing (ICICSP), pp. 280\u2013284. IEEE (2022)","DOI":"10.1109\/ICICSP55539.2022.10050624"},{"key":"5_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"}],"container-title":["Lecture Notes in Computer Science","Computational Mathematics Modeling in Cancer Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45087-7_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,10]],"date-time":"2023-12-10T14:02:00Z","timestamp":1702216920000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45087-7_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031450860","9783031450877"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45087-7_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"8 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CMMCA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Computational Mathematics Modeling in Cancer Analysis","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":"8 October 2023","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":"cmmca2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cmmca.github.io\/cmmca2023\/","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":"25","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":"17","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":"68% - 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":"2","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":"3","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)"}}]}}