{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:41:15Z","timestamp":1759358475103,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":29,"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_48","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T15:02:49Z","timestamp":1601650969000},"page":"497-507","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study"],"prefix":"10.1007","author":[{"given":"Jiancheng","family":"Yang","sequence":"first","affiliation":[]},{"given":"Mingze","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Kaiming","family":"Kuang","sequence":"additional","affiliation":[]},{"given":"Bingbing","family":"Ni","sequence":"additional","affiliation":[]},{"given":"Yunlang","family":"She","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Chang","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"issue":"2","key":"48_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)","journal-title":"Med. Phys."},{"issue":"6","key":"48_CR2","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3322\/caac.21492","volume":"68","author":"F Bray","year":"2018","unstructured":"Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68(6), 394\u2013424 (2018)","journal-title":"CA Cancer J. Clin."},{"key":"48_CR3","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S.J.: Class-balanced loss based on effective number of samples. In: CVPR, pp. 9260\u20139269 (2019)","DOI":"10.1109\/CVPR.2019.00949"},{"key":"48_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-66179-7_72","volume-title":"Medical Image Computing and Computer Assisted Intervention - MICCAI 2017","author":"Q Dou","year":"2017","unstructured":"Dou, Q., Chen, H., Jin, Y., Lin, H., Qin, J., Heng, P.-A.: Automated pulmonary nodule detection via 3D ConvNets with online sample filtering and hybrid-loss residual learning. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 630\u2013638. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_72"},{"key":"48_CR5","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115\u2013118 (2017)","journal-title":"Nature"},{"key":"48_CR6","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: ICML (2017)"},{"key":"48_CR7","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1198\/016214506000001437","volume":"102","author":"T Gneiting","year":"2007","unstructured":"Gneiting, T., Raftery, A.E.: Strictly proper scoring rules, prediction, and estimation. J. Am. Stat. Assoc. 102, 359\u2013378 (2007)","journal-title":"J. Am. Stat. Assoc."},{"key":"48_CR8","doi-asserted-by":"publisher","first-page":"1847","DOI":"10.1007\/s00330-019-06533-w","volume":"30","author":"J Gong","year":"2019","unstructured":"Gong, J., et al.: A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images. Eur. Radiol. 30, 1847\u20131855 (2019). https:\/\/doi.org\/10.1007\/s00330-019-06533-w","journal-title":"Eur. Radiol."},{"key":"48_CR9","doi-asserted-by":"crossref","unstructured":"Guan, M.Y., Gulshan, V., Dai, A.M., Hinton, G.E.: Who said what: modeling individual labelers improves classification. In: AAAI (2017)","DOI":"10.1609\/aaai.v32i1.11756"},{"key":"48_CR10","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: ICML (2017)"},{"key":"48_CR11","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR, vol. 1, p. 3 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"48_CR12","unstructured":"Huang, X., Yang, J., et al.: Evaluating and boosting uncertainty quantification in classification. arXiv preprint arXiv:1909.06030 (2019)"},{"key":"48_CR13","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":"48_CR14","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML (2015)"},{"issue":"6","key":"48_CR15","doi-asserted-by":"publisher","first-page":"3295","DOI":"10.1007\/s00330-019-06628-4","volume":"30","author":"H Kim","year":"2020","unstructured":"Kim, H., et al.: CT-based deep learning model to differentiate invasive pulmonary adenocarcinomas appearing as subsolid nodules among surgical candidates: comparison of the diagnostic performance with a size-based logistic model and radiologists. Eur. Radiol. 30(6), 3295\u20133305 (2020). https:\/\/doi.org\/10.1007\/s00330-019-06628-4","journal-title":"Eur. Radiol."},{"key":"48_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"48_CR17","doi-asserted-by":"crossref","unstructured":"Lee, K., Lee, K., Min, K., Zhang, Y., Shin, J., Lee, H.: Hierarchical novelty detection for visual object recognition. In: CVPR, pp. 1034\u20131042 (2018)","DOI":"10.1109\/CVPR.2018.00114"},{"key":"48_CR18","unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch (2017)"},{"key":"48_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1007\/978-3-030-32226-7_30","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"H Tang","year":"2019","unstructured":"Tang, H., Zhang, C., Xie, X.: NoduleNet: decoupled false positive reduction for pulmonary nodule detection and segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 266\u2013274. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_30"},{"issue":"9","key":"48_CR20","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1097\/JTO.0000000000000630","volume":"10","author":"WD Travis","year":"2015","unstructured":"Travis, W.D., et al.: The 2015 world health organization classification of lung tumors: impact of genetic, clinical and radiologic advances since the 2004 classification. J. Thorac. Oncol. 10(9), 1243\u20131260 (2015)","journal-title":"J. Thorac. Oncol."},{"key":"48_CR21","first-page":"1","volume":"53","author":"S Wang","year":"2019","unstructured":"Wang, S., et al.: Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning. Eur. Respir. J. 53, 1\u201311 (2019)","journal-title":"Eur. Respir. J."},{"key":"48_CR22","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 - MICCAI 2017","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":"48_CR23","doi-asserted-by":"crossref","unstructured":"Yang, J., Deng, H., Huang, X., Ni, B., Xu, Y.: Relational learning between multiple pulmonary nodules via deep set attention transformers. In: ISBI (2020)","DOI":"10.1109\/ISBI45749.2020.9098722"},{"key":"48_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"},{"key":"48_CR25","unstructured":"Yang, J., Huang, X., Ni, B., Xu, J., Yang, C., Xu, G.: Reinventing 2D convolutions for 3D medical images. arXiv preprint arXiv:1911.10477 (2019)"},{"key":"48_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: Context encoding for semantic segmentation. In: CVPR, June 2018","DOI":"10.1109\/CVPR.2018.00747"},{"key":"48_CR27","doi-asserted-by":"publisher","first-page":"3532","DOI":"10.1002\/cam4.2233","volume":"8","author":"W Zhao","year":"2019","unstructured":"Zhao, W., et al.: Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning. Cancer Med. 8, 3532\u20133543 (2019)","journal-title":"Cancer Med."},{"issue":"24","key":"48_CR28","doi-asserted-by":"publisher","first-page":"6881","DOI":"10.1158\/0008-5472.CAN-18-0696","volume":"78","author":"W Zhao","year":"2018","unstructured":"Zhao, W., et al.: 3D deep learning from CT scans predicts tumor invasiveness of subcentimeter pulmonary adenocarcinomas. Cancer Res. 78(24), 6881\u20136889 (2018)","journal-title":"Cancer Res."},{"issue":"10","key":"48_CR29","doi-asserted-by":"publisher","first-page":"1893","DOI":"10.1111\/1759-7714.13161","volume":"10","author":"W Zhao","year":"2019","unstructured":"Zhao, W., et al.: Convolution kernel and iterative reconstruction affect the diagnostic performance of radiomics and deep learning in lung adenocarcinoma pathological subtypes. Thorac. Cancer 10(10), 1893\u20131903 (2019)","journal-title":"Thorac. Cancer"}],"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_48","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:07:12Z","timestamp":1759356432000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59725-2_48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597245","9783030597252"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59725-2_48","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)"}}]}}