{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:02:46Z","timestamp":1742958166498,"version":"3.40.3"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030873578"},{"type":"electronic","value":"9783030873585"}],"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\/978-3-030-87358-5_66","type":"book-chapter","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T23:54:11Z","timestamp":1632959651000},"page":"809-819","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Corona Virus Disease (COVID-19) Detection in CT Images Using Synergic Deep Learning"],"prefix":"10.1007","author":[{"given":"Yiwei","family":"Gao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjie","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huafeng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"issue":"2","key":"66_CR1","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1007\/s13246-020-00865-4","volume":"43","author":"ID Apostolopoulos","year":"2020","unstructured":"Apostolopoulos, I.D., Mpesiana, T.A.: COVID-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys. Eng. Sci. Med. 43(2), 635\u2013640 (2020)","journal-title":"Phys. Eng. Sci. Med."},{"key":"66_CR2","doi-asserted-by":"crossref","unstructured":"\u00d6zkaya, U. Barstugan, M., Ozturk, S.: Coronavirus (COVID-19) classification using CT images by machine learning methods. arXiv (2020)","DOI":"10.1007\/978-3-030-55258-9_17"},{"issue":"8","key":"66_CR3","doi-asserted-by":"publisher","first-page":"2615","DOI":"10.1109\/TMI.2020.2995965","volume":"39","author":"X Wang","year":"2020","unstructured":"Wang, X., et al.: A weakly-supervised framework for COVID-19 classification and Lesion localization from chest CT. IEEE Trans. Med. Imaging 39(8), 2615\u20132625 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"66_CR4","doi-asserted-by":"publisher","first-page":"2584","DOI":"10.1109\/TMI.2020.2996256","volume":"39","author":"Z Han","year":"2020","unstructured":"Han, Z., et al.: Accurate screening of COVID-19 using attention-based deep 3D multiple instance learning. IEEE Trans. Med. Imaging 39(8), 2584\u20132594 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"66_CR5","doi-asserted-by":"crossref","unstructured":"Zhou, L., Li, Z., Zhou, J., Li, H., Gao, X.: A rapid, accurate and machine-agnostic segmentation and quantification method for CT-based COVID-19 diagnosis. IEEE Trans. Med. Imaging 39(8), 2638\u20132652 (2020)","DOI":"10.1109\/TMI.2020.3001810"},{"key":"66_CR6","doi-asserted-by":"crossref","unstructured":"Roy, S., Menapace, W., Oei, S., Luijten, B., Demi, L.: Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound. IEEE Trans. Med. Imaging 39(8), 2676\u20132687 (2020)","DOI":"10.1109\/TMI.2020.2994459"},{"issue":"8","key":"66_CR7","doi-asserted-by":"publisher","first-page":"2688","DOI":"10.1109\/TMI.2020.2993291","volume":"39","author":"Y Oh","year":"2020","unstructured":"Oh, Y., Park, S., Ye, J.C.: Deep learning COVID-19 features on CXR using limited training data sets. IEEE Trans. Med. Imaging 39(8), 2688\u20132700 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"66_CR8","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1148\/radiol.2019190938","volume":"293","author":"P Schelb","year":"2019","unstructured":"Schelb, P., et al.: Classification of cancer at prostate MRI: deep learning versus clinical PI-RADS assessment. Radiology 293(3), 607\u2013617 (2019)","journal-title":"Radiology"},{"key":"66_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1007\/978-3-030-00934-2_2","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"J Zhang","year":"2018","unstructured":"Zhang, J., Xie, Y., Wu, Q., Xia, Y.: Skin Lesion classification in dermoscopy images using synergic deep learning. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 12\u201320. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_2"},{"key":"66_CR10","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.media.2019.02.010","volume":"54","author":"J Zhang","year":"2019","unstructured":"Zhang, J., Xie, Y., Wu, Q., Xia, Y.: Medical image classification using synergic deep learning. Med. Image Anal. 54, 10\u201319 (2019)","journal-title":"Med. Image Anal."},{"key":"66_CR11","doi-asserted-by":"crossref","unstructured":"Butt, C., Gill, J., Chun, D., Babu, B.A.: Deep learning system to screen coronavirus disease 2019 pneumonia. Appl. Intell. 5 (2020)","DOI":"10.1007\/s10489-020-01714-3"},{"key":"66_CR12","doi-asserted-by":"crossref","unstructured":"Wang, S., et al.: A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). medRxiv, p. 2020.02.14.20023028 (2020)","DOI":"10.1101\/2020.02.14.20023028"}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87358-5_66","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T19:55:42Z","timestamp":1673380542000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87358-5_66"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030873578","9783030873585"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87358-5_66","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"30 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Haikou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"6 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icig2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icig2021.csig.org.cn\/","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":"421","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":"198","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":"47% - 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":"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)"}},{"value":"Conference was postponed due to the COVID19 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)"}}]}}