{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T20:31:07Z","timestamp":1757622667879,"version":"3.44.0"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030865139"},{"type":"electronic","value":"9783030865146"}],"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\/978-3-030-86514-6_18","type":"book-chapter","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T12:05:38Z","timestamp":1631189138000},"page":"287-301","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["COVID Edge-Net: Automated COVID-19 Lung Lesion Edge Detection in Chest CT Images"],"prefix":"10.1007","author":[{"given":"Kang","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Dou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Wen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zikai","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Zhu, N., et al.: A novel coronavirus from patients with pneumonia in China, 2019. N. Engl. J. Med. 382(8) (2020)","DOI":"10.1056\/NEJMoa2001017"},{"issue":"10223","key":"18_CR2","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1016\/S0140-6736(20)30185-9","volume":"395","author":"C Wang","year":"2020","unstructured":"Wang, C., Horby, P.W., Hayden, F.G., Gao, G.F.: A novel coronavirus outbreak of global health concern. Lancet 395(10223), 470\u2013473 (2020)","journal-title":"Lancet"},{"issue":"1","key":"18_CR3","doi-asserted-by":"publisher","first-page":"E216","DOI":"10.1148\/radiol.2020201629","volume":"297","author":"M Oudkerk","year":"2020","unstructured":"Oudkerk, M., B\u00fcller, H.R., Kuijpers, D., et al.: Diagnosis, prevention, and treatment of thromboembolic complications in COVID-19: report of the national institute for public health of the Netherlands. Radiology 297(1), E216\u2013E222 (2020)","journal-title":"Radiology"},{"key":"18_CR4","unstructured":"Coronavirus COVID-19 global cases by the center for systems science and engineering at johns Hopkins university. https:\/\/coronavirus.jhu.edu\/map.html. Accessed 24 November 2020"},{"key":"18_CR5","unstructured":"Liang, T., et al.: Handbook of COVID-19 prevention and treatment. The first affiliated hospital, Zhejiang university school of medicine. Compil. Accord. Clin. Exp. 68 (2020)"},{"key":"18_CR6","unstructured":"Shan, F., et al.: Lung infection quantification of COVID-19 in CT images with deep learning. arXiv preprint arXiv:2003.04655 (2020)"},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Fang, Y., et al.: Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology 296(2), E115\u2013E117 (2020)","DOI":"10.1148\/radiol.2020200432"},{"issue":"2","key":"18_CR8","doi-asserted-by":"publisher","first-page":"E32","DOI":"10.1148\/radiol.2020200642","volume":"296","author":"T Ai","year":"2020","unstructured":"Ai, T., et al.: Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in china: a report of 1014 cases. Radiology 296(2), E32\u2013E40 (2020)","journal-title":"Radiology"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Ng, M.Y., et al.: Imaging profile of the COVID-19 infection: radiologic findings and literature review. Radiol. Cardiothorac. Imaging 2(1), e200034 (2020)","DOI":"10.1148\/ryct.2020200034"},{"issue":"8","key":"18_CR10","doi-asserted-by":"publisher","first-page":"2606","DOI":"10.1109\/TMI.2020.2992546","volume":"39","author":"H Kang","year":"2020","unstructured":"Kang, H., et al.: Diagnosis of coronavirus disease 2019 (COVID-19) with structured latent multi-view representation learning. IEEE Trans. Med. Imaging 39(8), 2606\u20132614 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"18_CR11","doi-asserted-by":"publisher","first-page":"2572","DOI":"10.1109\/TMI.2020.2994908","volume":"39","author":"J Wang","year":"2020","unstructured":"Wang, J., et al.: Prior-attention residual learning for more discriminative COVID-19 screening in CT images. IEEE Trans. Med. Imaging 39(8), 2572\u20132583 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"18_CR12","doi-asserted-by":"crossref","unstructured":"Hu, Y., Chen, Y., Li, X., Feng, J.: Dynamic feature fusion for semantic edge detection. arXiv preprint arXiv:1902.09104 (2019)","DOI":"10.24963\/ijcai.2019\/110"},{"issue":"8","key":"18_CR13","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"},{"key":"18_CR14","doi-asserted-by":"publisher","unstructured":"Yu, Z., Feng, C., Liu, M.Y., Ramalingam, S.: CASENet: deep category-aware semantic edge detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5964\u20135973 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.191","DOI":"10.1109\/CVPR.2017.191"},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Qiu, Y., Liu, Y., Xu, J.: Miniseg: An extremely minimum network for efficient covid-19 segmentation. arXiv preprint arXiv:2004.09750 (2020)","DOI":"10.1609\/aaai.v35i6.16617"},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Does non-COVID19 lung lesion help? investigating transferability in COVID-19 CT image segmentation. arXiv preprint arXiv:2006.13877 (2020)","DOI":"10.1016\/j.cmpb.2021.106004"},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"M\u00fcller, D., Rey, I.S., Kramer, F.: Automated chest CT image segmentation of COVID-19 lung infection based on 3d u-net. arXiv preprint arXiv:2007.04774 (2020)","DOI":"10.1016\/j.imu.2021.100681"},{"key":"18_CR18","unstructured":"Zhou, T., Canu, S., Ruan, S.: An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism. arXiv preprint arXiv:2004.06673 (2020)"},{"key":"18_CR19","unstructured":"Chen, X., Yao, L., Zhang, Y.: Residual attention u-net for automated multi-class segmentation of COVID-19 chest CT images. arXiv preprint arXiv:2004.05645 (2020)"},{"key":"18_CR20","unstructured":"COVID-19 CT segmentation dataset. https:\/\/medicalsegmentation.com\/covid19\/"},{"key":"18_CR21","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"18_CR22","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"18_CR23","series-title":"Informatik aktuell","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-662-54345-0_3","volume-title":"Bildverarbeitung f\u00fcr die Medizin 2017","author":"O Ronneberger","year":"2017","unstructured":"Ronneberger, O.: Invited talk: u-net convolutional networks for biomedical image segmentation. In: Bildverarbeitung f\u00fcr die Medizin 2017. I, pp. 3\u20133. Springer, Heidelberg (2017). https:\/\/doi.org\/10.1007\/978-3-662-54345-0_3"},{"key":"18_CR24","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"},{"key":"18_CR25","unstructured":"Oktay, O., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"},{"issue":"12","key":"18_CR26","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","volume":"37","author":"X Li","year":"2018","unstructured":"Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.W., Heng, P.A.: H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imaging 37(12), 2663\u20132674 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"18_CR27","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","volume":"53","author":"J Schlemper","year":"2019","unstructured":"Schlemper, J., et al.: Attention gated networks: learning to leverage salient regions in medical images. Med. Image Anal. 53, 197\u2013207 (2019)","journal-title":"Med. Image Anal."},{"key":"18_CR28","doi-asserted-by":"crossref","unstructured":"Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 8(6), 679\u2013698 (1986)","DOI":"10.1109\/TPAMI.1986.4767851"},{"key":"18_CR29","doi-asserted-by":"publisher","unstructured":"Woo, S., Park, J., Lee, J.Y., So Kweon, I.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"18_CR30","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11534\u201311542 (2020)","DOI":"10.1109\/CVPR42600.2020.01155"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86514-6_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T22:02:55Z","timestamp":1757368975000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86514-6_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030865139","9783030865146"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86514-6_18","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":"10 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bilbao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2021.ecmlpkdd.org\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"869","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":"210","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":"24% - 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":"3-4","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":"3-9","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":"The conference was held online 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)"}}]}}