{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T01:47:02Z","timestamp":1726105622299},"publisher-location":"Cham","reference-count":12,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030638191"},{"type":"electronic","value":"9783030638207"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-63820-7_67","type":"book-chapter","created":{"date-parts":[[2020,11,18]],"date-time":"2020-11-18T09:12:07Z","timestamp":1605690727000},"page":"589-596","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Unsupervised Reused Convolutional Network for Metal Artifact Reduction"],"prefix":"10.1007","author":[{"given":"Binyu","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Jinbao","family":"Li","sequence":"additional","affiliation":[]},{"given":"Qianqian","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Yingli","family":"Zhong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,17]]},"reference":[{"key":"67_CR1","unstructured":"Gjesteby, L., et al.: Deep neural network for ct metal artifact reduction with a perceptual loss function. In: Proceedings of The Fifth International Conference on Image Formation in X-ray Computed Tomography (2018)"},{"key":"67_CR2","doi-asserted-by":"crossref","unstructured":"Sakamoto, M., et al.: Automated segmentation of hip and thigh muscles in metal artifact contaminated CT using CNN. In: International Forum on Medical Imaging in Asia 2019 (2019)","DOI":"10.1117\/12.2521440"},{"key":"67_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1007\/978-3-030-32226-7_36","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"T Shen","year":"2019","unstructured":"Shen, T., Li, X., Zhong, Z., Wu, J., Lin, Z.: R$$^{2}$$-Net: recurrent and recursive network for sparse-view CT artifacts removal. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 319\u2013327. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_36"},{"key":"67_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/978-3-030-32226-7_23","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"H Liao","year":"2019","unstructured":"Liao, H., Lin, W.-A., Yuan, J., Zhou, S.K., Luo, J.: Artifact disentanglement network for unsupervised metal artifact reduction. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 203\u2013211. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_23"},{"key":"67_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00928-1_1","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"J Wang","year":"2018","unstructured":"Wang, J., Zhao, Y., Noble, J.H., Dawant, B.M.: Conditional generative adversarial networks for metal artifact reduction in CT images of the ear. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_1"},{"key":"67_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/978-3-030-32226-7_14","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Z Wang","year":"2019","unstructured":"Wang, Z., et al.: Deep learning based metal artifacts reduction in post-operative cochlear implant CT imaging. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 121\u2013129. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_14"},{"issue":"6","key":"67_CR7","doi-asserted-by":"publisher","first-page":"1370","DOI":"10.1109\/TMI.2018.2823083","volume":"37","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Yu, H.: Convolutional neural network based metal artifact reduction in X-ray computed tomography. IEEE Trans. Med. Imaging 37(6), 1370\u20131381 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"67_CR8","unstructured":"Goodfellow, I.J., et al.: Generative adversarial networks. arXiv:1406.2661 (2014)"},{"issue":"3","key":"67_CR9","doi-asserted-by":"publisher","first-page":"036501","DOI":"10.1117\/1.JMI.5.3.036501","volume":"5","author":"K Yan","year":"2018","unstructured":"Yan, K., Wang, X., Lu, L., Summers, R.M.: DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J. Med. Imaging. 5(3), 036501 (2018)","journal-title":"J. Med. Imaging."},{"key":"67_CR10","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, pp. 8026\u20138037 (2019)"},{"key":"67_CR11","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1109\/TCI.2019.2937221","volume":"6","author":"M-U Ghani","year":"2020","unstructured":"Ghani, M.-U., Karl, W.-C.: Fast enhanced CT metal artifact reduction using data domain deep learning. IEEE Trans. Comput. Imaging 6, 181\u2013193 (2020)","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"67_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/978-3-030-59713-9_15","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"Y Lyu","year":"2020","unstructured":"Lyu, Y., Lin, W.-A., Liao, H., Lu, J., Zhou, S.K.: Encoding metal mask projection for metal artifact reduction in computed tomography. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 147\u2013157. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59713-9_15"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-63820-7_67","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T22:26:35Z","timestamp":1619303195000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-63820-7_67"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030638191","9783030638207"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-63820-7_67","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"17 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bangkok","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thailand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.apnns.org\/ICONIP2020","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"618","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":"187","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":"189","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":"30% - 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":"3.18","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":"3.68","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":"Due to COVID-19 pandemic the conference was held virtually.","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)"}}]}}