{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:41:53Z","timestamp":1759358513884,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030597245"},{"type":"electronic","value":"9783030597252"}],"license":[{"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\/978-3-030-59725-2_58","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T15:02:49Z","timestamp":1601650969000},"page":"599-608","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Nodule2vec: A 3D Deep Learning System for Pulmonary Nodule Retrieval Using Semantic Representation"],"prefix":"10.1007","author":[{"given":"Ilia","family":"Kravets","sequence":"first","affiliation":[]},{"given":"Tal","family":"Heletz","sequence":"additional","affiliation":[]},{"given":"Hayit","family":"Greenspan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"issue":"2","key":"58_CR1","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"38","author":"SG Armato","year":"2011","unstructured":"Armato, 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). https:\/\/doi.org\/10.1118\/1.3528204","journal-title":"Med. Phys."},{"key":"58_CR2","doi-asserted-by":"publisher","unstructured":"Armato III, S.G., et al.: Data from LIDC-IDRI (2015). https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.LO9QL9SX. https:\/\/wiki.cancerimagingarchive.net\/x\/rgAe","DOI":"10.7937\/K9\/TCIA.2015.LO9QL9SX"},{"issue":"1","key":"58_CR3","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/s10278-016-9904-y","volume":"30","author":"AK Dhara","year":"2016","unstructured":"Dhara, A.K., Mukhopadhyay, S., Dutta, A., Garg, M., Khandelwal, N.: Content-based image retrieval system for pulmonary nodules: assisting radiologists in self-learning and diagnosis of lung cancer. J. Digit. Imaging 30(1), 63\u201377 (2016). https:\/\/doi.org\/10.1007\/s10278-016-9904-y","journal-title":"J. Digit. Imaging"},{"issue":"4","key":"58_CR4","doi-asserted-by":"publisher","first-page":"044504","DOI":"10.1117\/1.jmi.3.4.044504","volume":"3","author":"MC Hancock","year":"2016","unstructured":"Hancock, M.C., Magnan, J.F.: Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the lung image database consortium dataset with two statistical learning methods. J. Med. Imaging 3(4), 044504 (2016). https:\/\/doi.org\/10.1117\/1.jmi.3.4.044504","journal-title":"J. Med. Imaging"},{"key":"58_CR5","unstructured":"Kaggle data science bowl 2017 (2017). https:\/\/www.kaggle.com\/c\/data-science-bowl-2017. Accessed January 2020"},{"issue":"S1","key":"58_CR6","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/s10278-007-9059-y","volume":"20","author":"MO Lam","year":"2007","unstructured":"Lam, M.O., Disney, T., Raicu, D.S., Furst, J., Channin, D.S.: BRISC\u2014an open source pulmonary nodule image retrieval framework. J. Digit. Imaging 20(S1), 63\u201371 (2007). https:\/\/doi.org\/10.1007\/s10278-007-9059-y","journal-title":"J. Digit. Imaging"},{"issue":"11","key":"58_CR7","doi-asserted-by":"publisher","first-page":"3484","DOI":"10.1109\/tnnls.2019.2892409","volume":"30","author":"F Liao","year":"2019","unstructured":"Liao, F., Liang, M., Li, Z., Hu, X., Song, S.: 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). https:\/\/doi.org\/10.1109\/tnnls.2019.2892409","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"58_CR8","doi-asserted-by":"publisher","unstructured":"Loyman, M., Greenspan, H.: Lung nodule retrieval using semantic similarity estimates. In: Hahn, H.K., Mori, K. (eds.) Medical Imaging 2019: Computer-Aided Diagnosis. SPIE, March 2019. https:\/\/doi.org\/10.1117\/12.2512115","DOI":"10.1117\/12.2512115"},{"key":"58_CR9","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"issue":"12","key":"58_CR10","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.1016\/j.acra.2007.07.021","volume":"14","author":"MF McNitt-Gray","year":"2007","unstructured":"McNitt-Gray, M.F., et al.: The lung image database consortium (LIDC) data collection process for nodule detection and annotation. Acad. Radiol. 14(12), 1464\u20131474 (2007). https:\/\/doi.org\/10.1016\/j.acra.2007.07.021","journal-title":"Acad. Radiol."},{"key":"58_CR11","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013)"},{"key":"58_CR12","volume-title":"Foundations of Machine Learning","author":"M Mohri","year":"2018","unstructured":"Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of Machine Learning, 2nd edn. The MIT Press, Cambridge (2018)","edition":"2"},{"key":"58_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2016\/3162649","volume":"2016","author":"L Pan","year":"2016","unstructured":"Pan, L., Qiang, Y., Yuan, J., Wu, L.: Rapid retrieval of lung nodule CT images based on hashing and pruning methods. BioMed Res. Int. 2016, 1\u201310 (2016). https:\/\/doi.org\/10.1155\/2016\/3162649","journal-title":"BioMed Res. Int."},{"key":"58_CR14","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"},{"key":"58_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2017.06.015","volume":"42","author":"AAA Setio","year":"2017","unstructured":"Setio, A.A.A., et al.: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Med. Image Anal. 42, 1\u201313 (2017). https:\/\/doi.org\/10.1016\/j.media.2017.06.015","journal-title":"Med. Image Anal."},{"issue":"1","key":"58_CR16","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21590","volume":"70","author":"RL Siegel","year":"2020","unstructured":"Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2020. CA: A Cancer J. Clin. 70(1), 7\u201330 (2020). https:\/\/doi.org\/10.3322\/caac.21590","journal-title":"CA: A Cancer J. Clin."},{"issue":"301","key":"58_CR17","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1080\/01621459.1963.10500845","volume":"58","author":"JH Ward","year":"1963","unstructured":"Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236\u2013244 (1963). https:\/\/doi.org\/10.1080\/01621459.1963.10500845","journal-title":"J. Am. Stat. Assoc."},{"key":"58_CR18","doi-asserted-by":"publisher","unstructured":"Wei, G., Ma, H., Qian, W., Jiang, H., Zhao, X.: Content-based retrieval for lung nodule diagnosis using learned distance metric. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, July 2017. https:\/\/doi.org\/10.1109\/embc.2017.8037711","DOI":"10.1109\/embc.2017.8037711"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59725-2_58","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:07:34Z","timestamp":1759356454000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59725-2_58"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597245","9783030597252"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59725-2_58","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":"29 September 2020","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":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","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":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2020.org\/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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1809","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":"542","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":"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","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":"4","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 virtually 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)"}}]}}