{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T03:27:33Z","timestamp":1767065253983,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030505158"},{"type":"electronic","value":"9783030505165"}],"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"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-50516-5_32","type":"book-chapter","created":{"date-parts":[[2020,6,19]],"date-time":"2020-06-19T17:04:00Z","timestamp":1592586240000},"page":"369-377","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An Automated Workflow for Lung Nodule Follow-Up Recommendation Using Deep Learning"],"prefix":"10.1007","author":[{"given":"Krishna Chaitanya","family":"Kaluva","sequence":"first","affiliation":[]},{"given":"Kiran","family":"Vaidhya","sequence":"additional","affiliation":[]},{"given":"Abhijith","family":"Chunduru","sequence":"additional","affiliation":[]},{"given":"Sambit","family":"Tarai","sequence":"additional","affiliation":[]},{"given":"Sai Prasad Pranav","family":"Nadimpalli","sequence":"additional","affiliation":[]},{"given":"Suthirth","family":"Vaidya","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,6,17]]},"reference":[{"key":"32_CR1","unstructured":"Pedrosa, J., et al.: LNDb: a lung nodule database on computed tomography. arXiv preprint arXiv:1911.08434 (2019)"},{"key":"32_CR2","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3322\/caac.21492","volume":"68","author":"F Bray","year":"2018","unstructured":"Bray, F., et al.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Can. J. Clin. 68, 394\u2013424 (2018). https:\/\/doi.org\/10.3322\/caac.21492","journal-title":"CA Can. J. Clin."},{"key":"32_CR3","unstructured":"https:\/\/www.lung.org\/our-initiatives\/research\/monitoring-trends-in-lung-disease\/state-of-lung-cancer"},{"issue":"1","key":"32_CR4","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.jacr.2019.07.008","volume":"17","author":"JR Parikh","year":"2020","unstructured":"Parikh, J.R., et al.: Radiologist burnout according to surveyed radiology practice leaders. J. Am. Coll. Radiol. 17(1), 78\u201381 (2020)","journal-title":"J. Am. Coll. Radiol."},{"issue":"1","key":"32_CR5","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1148\/radiol.10101254","volume":"259","author":"S Singh","year":"2011","unstructured":"Singh, S., et al.: Evaluation of reader variability in the interpretation of follow-up CT scans at lung cancer screening. Radiology 259(1), 263\u2013270 (2011)","journal-title":"Radiology"},{"issue":"22","key":"32_CR6","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. J. Am. Med. Assoc. 316(22), 2402\u20132410 (2016)","journal-title":"J. Am. Med. Assoc."},{"key":"32_CR7","doi-asserted-by":"crossref","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. arXiv preprint arXiv:1702.05747 (2017)","DOI":"10.1016\/j.media.2017.07.005"},{"key":"32_CR8","unstructured":"Ren, S., He, K., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91\u201399 (2015)"},{"key":"32_CR9","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., et al.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)","DOI":"10.5244\/C.30.87"},{"issue":"2","key":"32_CR10","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"38","author":"III Armato","year":"2011","unstructured":"Armato III, S.G., et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915\u2013931 (2011)","journal-title":"Med. Phys."},{"key":"32_CR11","doi-asserted-by":"crossref","unstructured":"Holmes III, D., Bartholmai, B., Karwoski, R., Zavaletta, V., Robb, R.: The lung tissue research consortium: an extensive open database containing histological clinical and radiological data to study chronic lung disease. In: MICCAI Open Science Workshop (2006)","DOI":"10.54294\/hzdcno"},{"issue":"5","key":"32_CR12","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1056\/NEJMoa1102873","volume":"365","author":"National Lung Screening Trial Research Team","year":"2011","unstructured":"National Lung Screening Trial Research Team: Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 365(5), 395\u2013409 (2011)","journal-title":"N. Engl. J. Med."},{"key":"32_CR13","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":"11","key":"32_CR14","doi-asserted-by":"publisher","first-page":"3484","DOI":"10.1109\/TNNLS.2019.2892409","volume":"30","author":"F Liao","year":"2019","unstructured":"Liao, F., et al.: Evaluate the malignancy of pulmonary nodules using the 3-D deep leaky noisy-or network. IEEE Trans. Neural Netw. Learn. Syst. 30(11), 3484\u20133495 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"32_CR15","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., et al.: Focal loss for dense object detection. In: Proceedings of the IEEE international Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"32_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, Soeren S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, Mert R., Unal, G., Wells, W. (eds.) MICCAI 2016, Part II. LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Recognition"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-50516-5_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T17:03:17Z","timestamp":1696266197000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-50516-5_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030505158","9783030505165"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-50516-5_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"17 June 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"P\u00f3voa de Varzim","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","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":"24 June 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciar2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.aimiconf.org\/iciar20\/","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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"123","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":"54","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":"15","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":"44% - 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":"2,9","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":"3,8","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":"Due to the corona pandemic, ICIAR 2020 will be held virtually only.","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)"}}]}}