{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T10:10:18Z","timestamp":1783764618501,"version":"3.55.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164361","type":"print"},{"value":"9783031164378","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-16437-8_74","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T18:13:04Z","timestamp":1663265584000},"page":"770-779","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["LIDP: A Lung Image Dataset with\u00a0Pathological Information for\u00a0Lung Cancer Screening"],"prefix":"10.1007","author":[{"given":"Yanbo","family":"Shao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minghao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juanyun","family":"Mai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinliang","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayin","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoqi","family":"Diao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Airu","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianyu","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"You","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangcheng","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinsheng","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"74_CR1","doi-asserted-by":"publisher","first-page":"1815","DOI":"10.1007\/s11548-019-01981-7","volume":"10","author":"M Al-Shabi","year":"2019","unstructured":"Al-Shabi, M., Lan, B.L., Chan, W.Y., Ng, K.H., Tan, M.: Lung nodule classification using deep local-global networks. Int. J. Comput. Assist. Radiol. Surg. 10, 1815\u20131819 (2019)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"74_CR2","doi-asserted-by":"publisher","first-page":"178827","DOI":"10.1109\/ACCESS.2019.2958663","volume":"7","author":"M Al-Shabi","year":"2019","unstructured":"Al-Shabi, M., Lee, H.K., Tan, M.: Gated-dilated networks for lung nodule classification in CT scans. IEEE Access 7, 178827\u2013178838 (2019)","journal-title":"IEEE Access"},{"key":"74_CR3","doi-asserted-by":"publisher","first-page":"175859","DOI":"10.1109\/ACCESS.2020.3026080","volume":"8","author":"I Ali","year":"2020","unstructured":"Ali, I., Muzammil, M., Haq, I.U., Khaliq, A.A., Abdullah, S.: Efficient lung nodule classification using transferable texture convolutional neural network. IEEE Access 8, 175859\u2013175870 (2020)","journal-title":"IEEE Access"},{"key":"74_CR4","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"2","author":"SG Armato III","year":"2011","unstructured":"Armato, S.G., III., 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. 2, 915\u2013931 (2011)","journal-title":"Med. Phys."},{"issue":"2","key":"74_CR5","doi-asserted-by":"publisher","first-page":"118","DOI":"10.5152\/dir.2016.16187","volume":"23","author":"A Del Ciello","year":"2017","unstructured":"Del Ciello, A., Franchi, P., Contegiacomo, A., Cicchetti, G., Bonomo, L., Larici, A.R.: Missed lung cancer: when, where, and why? Diagn. Intervent. Radiol. 23(2), 118 (2017)","journal-title":"Diagn. Intervent. Radiol."},{"key":"74_CR6","doi-asserted-by":"crossref","unstructured":"Dey, R., Lu, Z., Hong, Y.: Diagnostic classification of lung nodules using 3D neural networks. In: 2018 IEEE 15th International Symposium on Biomedical Imaging, pp. 774\u2013778 (2018)","DOI":"10.1109\/ISBI.2018.8363687"},{"key":"74_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/978-3-319-59050-9_20","volume-title":"Information Processing in Medical Imaging","author":"S Hussein","year":"2017","unstructured":"Hussein, S., Cao, K., Song, Q., Bagci, U.: Risk stratification of lung nodules using 3D CNN-based multi-task learning. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 249\u2013260. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_20"},{"key":"74_CR8","doi-asserted-by":"crossref","unstructured":"Jiang, H., Shen, F., Gao, F., Han, W.: Learning efficient, explainable and discriminative representations for pulmonary nodules classification. Pattern Recogn. 107825 (2021)","DOI":"10.1016\/j.patcog.2021.107825"},{"key":"74_CR9","doi-asserted-by":"crossref","unstructured":"Kirby, J.S., et al.: LUNGx challenge for computerized lung nodule classification. J. Med. Imaging (4), 044506 (2016)","DOI":"10.1117\/1.JMI.3.4.044506"},{"key":"74_CR10","doi-asserted-by":"publisher","first-page":"77725","DOI":"10.1109\/ACCESS.2020.2987961","volume":"8","author":"Y Kuang","year":"2020","unstructured":"Kuang, Y., Lan, T., Peng, X., Selasi, G.E., Liu, Q., Zhang, J.: Unsupervised multi-discriminator generative adversarial network for lung nodule malignancy classification. IEEE Access 8, 77725\u201377734 (2020)","journal-title":"IEEE Access"},{"key":"74_CR11","doi-asserted-by":"crossref","unstructured":"Lei, Y., Tian, Y., Shan, H., Zhang, J., Wang, G., Kalra, M.K.: Shape and margin-aware lung nodule classification in low-dose CT images via soft activation mapping. Med. Image Anal. 101628 (2020)","DOI":"10.1016\/j.media.2019.101628"},{"key":"74_CR12","doi-asserted-by":"publisher","first-page":"3484","DOI":"10.1109\/TNNLS.2019.2892409","volume":"11","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. 11, 3484\u20133495 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"74_CR13","doi-asserted-by":"publisher","first-page":"49080","DOI":"10.1109\/ACCESS.2018.2865544","volume":"6","author":"Y Liu","year":"2018","unstructured":"Liu, Y., Hao, P., Zhang, P., Xu, X., Wu, J., Chen, W.: Dense convolutional binary-tree networks for lung nodule classification. IEEE Access 6, 49080\u201349088 (2018)","journal-title":"IEEE Access"},{"key":"74_CR14","doi-asserted-by":"crossref","unstructured":"Rorke, L.B.: Pathologic diagnosis as the gold standard (1997)","DOI":"10.1002\/(SICI)1097-0142(19970215)79:4<665::AID-CNCR1>3.0.CO;2-D"},{"key":"74_CR15","unstructured":"Shan, H., Wang, G., Kalra, M.K., de Souza, R., Zhang, J.: Enhancing transferability of features from pretrained deep neural networks for lung nodule classification. In: Proceedings of the 2017 International Conference on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (2017)"},{"key":"74_CR16","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1016\/j.patcog.2016.05.029","volume":"61","author":"W Shen","year":"2017","unstructured":"Shen, W., et al.: Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recogn. 61, 663\u2013673 (2017)","journal-title":"Pattern Recogn."},{"key":"74_CR17","doi-asserted-by":"crossref","unstructured":"Sung, H., et al.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J. Clin. 71(3), 209\u2013249 (2021)","DOI":"10.3322\/caac.21660"},{"key":"74_CR18","doi-asserted-by":"crossref","unstructured":"National Lung Screening Trial Research Team: Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 635(5), 395\u2013409 (2011)","DOI":"10.1056\/NEJMoa1102873"},{"key":"74_CR19","doi-asserted-by":"crossref","unstructured":"Wu, G.X., Raz, D.J.: Lung cancer screening. Lung Cancer 1\u201323 (2016)","DOI":"10.1007\/978-3-319-40389-2_1"},{"key":"74_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1007\/978-3-319-66179-7_75","volume-title":"Medical Image Computing and Computer Assisted Intervention","author":"Y Xie","year":"2017","unstructured":"Xie, Y., Xia, Y., Zhang, J., Feng, D.D., Fulham, M., Cai, W.: Transferable multi-model ensemble for benign-malignant lung nodule classification on chest CT. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 656\u2013664. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_75"},{"key":"74_CR21","first-page":"991","volume":"4","author":"Y Xie","year":"2018","unstructured":"Xie, Y., et al.: Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT. IEEE Trans. Med. Imaging 4, 991\u20131004 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"74_CR22","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.media.2019.07.004","volume":"57","author":"Y Xie","year":"2019","unstructured":"Xie, Y., Zhang, J., Xia, Y.: Semi-supervised adversarial model for benign-malignant lung nodule classification on chest CT. Med. Image Anal. 57, 237\u2013248 (2019)","journal-title":"Med. Image Anal."},{"key":"74_CR23","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.inffus.2017.10.005","volume":"42","author":"Y Xie","year":"2018","unstructured":"Xie, Y., Zhang, J., Xia, Y., Fulham, M., Zhang, Y.: Fusing texture, shape and deep model-learned information at decision level for automated classification of lung nodules on chest CT. Inf. Fusion 42, 102\u2013110 (2018)","journal-title":"Inf. Fusion"},{"key":"74_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1007\/978-3-030-32226-7_73","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"J Yang","year":"2019","unstructured":"Yang, J., Fang, R., Ni, B., Li, Y., Xu, Y., Li, L.: Probabilistic radiomics: ambiguous diagnosis with controllable shape analysis. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 658\u2013666. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_73"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16437-8_74","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T14:12:32Z","timestamp":1710252752000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16437-8_74"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164361","9783031164378"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16437-8_74","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 2022","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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","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 Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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":"5","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)"}}]}}