{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:22:55Z","timestamp":1743049375439,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031438974"},{"type":"electronic","value":"9783031438981"}],"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\/978-3-031-43898-1_58","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:23Z","timestamp":1696115303000},"page":"603-613","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Unpaired Cross-Modal Interaction Learning for\u00a0COVID-19 Segmentation on\u00a0Limited CT Images"],"prefix":"10.1007","author":[{"given":"Qingbiao","family":"Guan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yutong","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianpeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhibin","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"unstructured":"Akhloufi, M.A., Chetoui, M.: Chest XR COVID-19 detection (2021). https:\/\/cxr-covid19.grand-challenge.org\/. Accessed Sept 2021","key":"58_CR1"},{"key":"58_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/978-3-030-00919-9_7","volume-title":"Machine Learning in Medical Imaging","author":"X Cao","year":"2018","unstructured":"Cao, X., Yang, J., Wang, L., Xue, Z., Wang, Q., Shen, D.: Deep learning based inter-modality image registration supervised by intra-modality similarity. In: Shi, Y., Suk, H.-I., Liu, M. (eds.) MLMI 2018. LNCS, vol. 11046, pp. 55\u201363. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00919-9_7"},{"key":"58_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102506","volume":"80","author":"X Chen","year":"2022","unstructured":"Chen, X., Zhou, H.Y., Liu, F., Guo, J., Wang, L., Yu, Y.: Mass: modality-collaborative semi-supervised segmentation by exploiting cross-modal consistency from unpaired ct and mri images. Med. Image Anal. 80, 102506 (2022)","journal-title":"Med. Image Anal."},{"key":"58_CR4","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark, K., et al.: The cancer imaging archive (tcia): maintaining and operating a public information repository. J. Digit. Imaging 26, 1045\u20131057 (2013)","journal-title":"J. Digit. Imaging"},{"issue":"1","key":"58_CR5","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1038\/s41597-020-00741-6","volume":"7","author":"S Desai","year":"2020","unstructured":"Desai, S., et al.: Chest imaging representing a covid-19 positive rural us population. Sci. Data 7(1), 414 (2020)","journal-title":"Sci. Data"},{"issue":"7","key":"58_CR6","doi-asserted-by":"publisher","first-page":"2415","DOI":"10.1109\/TMI.2019.2963882","volume":"39","author":"Q Dou","year":"2020","unstructured":"Dou, Q., Liu, Q., Heng, P.A., Glocker, B.: Unpaired multi-modal segmentation via knowledge distillation. IEEE Trans. Med. Imaging 39(7), 2415\u20132425 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"58_CR7","doi-asserted-by":"publisher","first-page":"2626","DOI":"10.1109\/TMI.2020.2996645","volume":"39","author":"DP Fan","year":"2020","unstructured":"Fan, D.P., et al.: Inf-net: automatic covid-19 lung infection segmentation from ct images. IEEE Trans. Med. Imaging 39(8), 2626\u20132637 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"58_CR8","doi-asserted-by":"publisher","first-page":"4080","DOI":"10.1038\/s41467-020-17971-2","volume":"11","author":"SA Harmon","year":"2020","unstructured":"Harmon, S.A., et al.: Artificial intelligence for the detection of covid-19 pneumonia on chest ct using multinational datasets. Nat. Commun. 11(1), 4080 (2020)","journal-title":"Nat. Commun."},{"doi-asserted-by":"publisher","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: swin transformers for semantic segmentation of brain tumors in mri images. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, 27 September 2021, Revised Selected Papers, Part I, pp. 272\u2013284. Springer, Heidelberg (2022). https:\/\/doi.org\/10.1007\/978-3-031-08999-2_22","key":"58_CR9","DOI":"10.1007\/978-3-031-08999-2_22"},{"issue":"2","key":"58_CR10","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"unstructured":"Loshchilov, I., Hutter, F.: Fixing weight decay regularization in adam (2018)","key":"58_CR11"},{"doi-asserted-by":"publisher","unstructured":"Lyu, J., Sui, B., Wang, C., Tian, Y., Dou, Q., Qin, J.: Dudocaf: dual-domain cross-attention fusion with recurrent transformer for fast multi-contrast mr imaging. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention-MICCAI 2022: 25th International Conference, Singapore, 18\u201322 September 2022, Proceedings, Part VI, pp. 474\u2013484. Springer, Heidelberg (2022). DOI: https:\/\/doi.org\/10.1007\/978-3-031-16446-0_45","key":"58_CR12","DOI":"10.1007\/978-3-031-16446-0_45"},{"key":"58_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1007\/978-3-030-59719-1_42","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"S Mo","year":"2020","unstructured":"Mo, S., et al.: Multimodal priors guided segmentation of liver lesions in MRI using mutual information based graph co-attention networks. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 429\u2013438. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59719-1_42"},{"doi-asserted-by":"crossref","unstructured":"Qiu, Y., Liu, Y., Li, S., Xu, J.: Miniseg: an extremely minimum network for efficient covid-19 segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4846\u20134854 (2021)","key":"58_CR14","DOI":"10.1609\/aaai.v35i6.16617"},{"key":"58_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102605","volume":"82","author":"HR Roth","year":"2022","unstructured":"Roth, H.R., et al.: Rapid artificial intelligence solutions in a pandemic-the covid-19-20 lung ct lesion segmentation challenge. Med. Image Anal. 82, 102605 (2022)","journal-title":"Med. Image Anal."},{"key":"58_CR16","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/RBME.2020.2987975","volume":"14","author":"F Shi","year":"2020","unstructured":"Shi, F., et al.: Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for covid-19. IEEE Rev. Biomed. Eng. 14, 4\u201315 (2020)","journal-title":"IEEE Rev. Biomed. Eng."},{"issue":"8","key":"58_CR17","doi-asserted-by":"publisher","first-page":"2653","DOI":"10.1109\/TMI.2020.3000314","volume":"39","author":"G Wang","year":"2020","unstructured":"Wang, G., et al.: A noise-robust framework for automatic segmentation of covid-19 pneumonia lesions from ct images. IEEE Trans. Med. Imaging 39(8), 2653\u20132663 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"doi-asserted-by":"crossref","unstructured":"Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: Chestx-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097\u20132106 (2017)","key":"58_CR18","DOI":"10.1109\/CVPR.2017.369"},{"key":"58_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/978-3-030-87199-4_16","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Y Xie","year":"2021","unstructured":"Xie, Y., Zhang, J., Shen, C., Xia, Y.: CoTr: efficiently bridging CNN and transformer for 3D medical image segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 171\u2013180. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87199-4_16"},{"key":"58_CR20","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1007\/978-3-031-19803-8_33","volume-title":"European Conference on Computer Vision","author":"Y Xie","year":"2022","unstructured":"Xie, Y., Zhang, J., Xia, Y., Wu, Q.: Unimiss: universal medical self-supervised learning via breaking dimensionality barrier. In: Avidan, S., Brostow, G., Cisse, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13681, pp. 558\u2013575. Springer, Heidelberg (2022). https:\/\/doi.org\/10.1007\/978-3-031-19803-8_33"},{"issue":"3","key":"58_CR21","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1109\/TMI.2020.3040950","volume":"40","author":"J Zhang","year":"2020","unstructured":"Zhang, J., et al.: Viral pneumonia screening on chest x-rays using confidence-aware anomaly detection. IEEE Trans. Med. Imaging 40(3), 879\u2013890 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"doi-asserted-by":"publisher","unstructured":"Zhang, Y., He, N., Yang, J., Li, Y., Wei, D., Huang, Y., Zhang, Y., He, Z., Zheng, Y.: mmformer: Multimodal medical transformer for incomplete multimodal learning of brain tumor segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention-MICCAI 2022: 25th International Conference, Singapore, 18\u201322 September 2022, Proceedings, Part V, pp. 107\u2013117. Springer, Heidelberg (2022). https:\/\/doi.org\/10.1007\/978-3-031-16443-9_11","key":"58_CR22","DOI":"10.1007\/978-3-031-16443-9_11"},{"key":"58_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1007\/978-3-030-87193-2_56","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., et al.: Modality-aware mutual learning for multi-modal medical image segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 589\u2013599. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_56"},{"unstructured":"Zhou, H.Y., Guo, J., Zhang, Y., Yu, L., Wang, L., Yu, Y.: nnformer: interleaved transformer for volumetric segmentation. arXiv preprint arXiv:2109.03201 (2021)","key":"58_CR24"}],"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\/978-3-031-43898-1_58","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T14:26:39Z","timestamp":1710167199000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43898-1_58"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031438974","9783031438981"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43898-1_58","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":"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)"}}]}}