{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T02:16:33Z","timestamp":1769825793111,"version":"3.49.0"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030781903","type":"print"},{"value":"9783030781910","type":"electronic"}],"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\/978-3-030-78191-0_47","type":"book-chapter","created":{"date-parts":[[2021,6,20]],"date-time":"2021-06-20T06:02:29Z","timestamp":1624168949000},"page":"611-623","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Semi-Supervised Screening of COVID-19 from Positive and Unlabeled Data with Constraint Non-Negative Risk Estimator"],"prefix":"10.1007","author":[{"given":"Zhongyi","family":"Han","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rundong","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benzheng","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yilong","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,14]]},"reference":[{"key":"47_CR1","unstructured":"Arora, S., Ge, R., Liang, Y., Ma, T., Zhang, Y.: Generalization and equilibrium in generative adversarial nets (gans). CoRR abs\/1703.00573 (2017). http:\/\/arxiv.org\/abs\/1703.00573"},{"key":"47_CR2","unstructured":"Christoffel, M., Niu, G., Sugiyama, M.: Class-prior estimation for learning from positive and unlabeled data. In: Asian Conference on Machine Learning, pp. 221\u2013236 (2016)"},{"key":"47_CR3","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/3-540-46769-6_18","volume-title":"Algorithmic Learning Theory","author":"F De Comit\u00e9","year":"1999","unstructured":"De Comit\u00e9, F., Denis, F., Gilleron, R., Letouzey, F.: Positive and unlabeled examples help learning. In: Watanabe, O., Yokomori, T. (eds.) ALT 1999. LNCS (LNAI), vol. 1720, pp. 219\u2013230. Springer, Heidelberg (1999). https:\/\/doi.org\/10.1007\/3-540-46769-6_18"},{"key":"47_CR4","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1007\/3-540-49730-7_9","volume-title":"Algorithmic Learning Theory","author":"F\u00c7 Denis","year":"1998","unstructured":"Denis, F.\u00c7.: PAC learning from positive statistical queries. In: Richter, M.M., Smith, C.H., Wiehagen, R., Zeugmann, T. (eds.) ALT 1998. LNCS (LNAI), vol. 1501, pp. 112\u2013126. Springer, Heidelberg (1998). https:\/\/doi.org\/10.1007\/3-540-49730-7_9"},{"key":"47_CR5","unstructured":"Du Plessis, M., Niu, G., Sugiyama, M.: Convex formulation for learning from positive and unlabeled data. In: International Conference on Machine Learning, pp. 1386\u20131394 (2015)"},{"key":"47_CR6","first-page":"703","volume":"27","author":"MC Du Plessis","year":"2014","unstructured":"Du Plessis, M.C., Niu, G., Sugiyama, M.: Analysis of learning from positive and unlabeled data. Adv. Neural Inf. Process. Syst. 27, 703\u2013711 (2014)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"47_CR7","doi-asserted-by":"crossref","unstructured":"Elkan, C., Noto, K.: Learning classifiers from only positive and unlabeled data. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 213\u2013220 (2008)","DOI":"10.1145\/1401890.1401920"},{"key":"47_CR8","unstructured":"Ghoshal, B., Tucker, A.: Estimating uncertainty and interpretability in deep learning for coronavirus (covid-19) detection. arXiv preprint arXiv:2003.10769 (2020)"},{"key":"47_CR9","unstructured":"Gozes, O., Frid-Adar, M., Sagie, N., Zhang, H., Ji, W., Greenspan, H.: Coronavirus detection and analysis on chest CT with deep learning. arXiv:2004.02640 (2020)"},{"key":"47_CR10","unstructured":"Gozes, O., et al.: Rapid AI development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis. arXiv preprint arXiv:2003.05037 (2020)"},{"issue":"8","key":"47_CR11","doi-asserted-by":"publisher","first-page":"2584","DOI":"10.1109\/TMI.2020.2996256","volume":"39","author":"Z Han","year":"2020","unstructured":"Han, Z., et al.: Accurate screening of covid-19 using attention-based deep 3D multiple instance learning. IEEE Trans. Med. Imaging 39(8), 2584\u20132594 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"47_CR12","doi-asserted-by":"crossref","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)","DOI":"10.1109\/CVPR.2016.90"},{"key":"47_CR13","doi-asserted-by":"crossref","unstructured":"Huang, L., et al.: Serial quantitative chest CT assessment of covid-19: deep-learning approach. Radiol. Cardiothorac. Imaging 2(2), e200075 (2020)","DOI":"10.1148\/ryct.2020200075"},{"key":"47_CR14","unstructured":"Kato, M., Xu, L., Niu, G., Sugiyama, M.: Alternate estimation of a classifier and the class-prior from positive and unlabeled data. arXiv preprint:1809.05710 (2018)"},{"issue":"5","key":"47_CR15","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany, D.S., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122\u20131131 (2018)","journal-title":"Cell"},{"key":"47_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization (2014)"},{"key":"47_CR17","unstructured":"Kiryo, R., Niu, G., Du Plessis, M.C., Sugiyama, M.: Positive-unlabeled learning with non-negative risk estimator. In: Advances in Neural Information Processing Systems, pp. 1675\u20131685 (2017)"},{"key":"47_CR18","unstructured":"Li, T., Han, Z., Wei, B., Zheng, Y., Hong, Y., Cong, J.: Robust screening of covid-19 from chest x-ray via discriminative cost-sensitive learning. arXiv preprint arXiv:2004.12592 (2020)"},{"key":"47_CR19","first-page":"587","volume":"3","author":"X Li","year":"2003","unstructured":"Li, X., Liu, B.: Learning to classify texts using positive and unlabeled data. IJCAI 3, 587\u2013592 (2003)","journal-title":"IJCAI"},{"key":"47_CR20","first-page":"2802","volume":"7","author":"X Li","year":"2007","unstructured":"Li, X., Liu, B., Ng, S.K.: Learning to identify unexpected instances in the test set. IJCAI 7, 2802\u20132807 (2007)","journal-title":"IJCAI"},{"key":"47_CR21","unstructured":"Liu, B., Dai, Y., Li, X., Lee, W.S., Yu, P.S.: Building text classifiers using positive and unlabeled examples. In: Third IEEE International Conference on Data Mining, pp. 179\u2013186. IEEE (2003)"},{"key":"47_CR22","first-page":"387","volume":"2","author":"B Liu","year":"2002","unstructured":"Liu, B., Lee, W.S., Yu, P.S., Li, X.: Partially supervised classification of text documents. ICML 2, 387\u2013394 (2002)","journal-title":"ICML"},{"key":"47_CR23","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Computational drug discovery with dyadic positive-unlabeled learning. In: Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 45\u201353. SIAM (2017)","DOI":"10.1137\/1.9781611974973.6"},{"issue":"1","key":"47_CR24","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1186\/1471-2105-12-389","volume":"12","author":"F Mordelet","year":"2011","unstructured":"Mordelet, F., Vert, J.P.: Prodige: prioritization of disease genes with multitask machine learning from positive and unlabeled examples. BMC Bioinformatics 12(1), 389 (2011)","journal-title":"BMC Bioinformatics"},{"issue":"3","key":"47_CR25","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/s10115-007-0107-1","volume":"16","author":"T Peng","year":"2008","unstructured":"Peng, T., Zuo, W., He, F.: SVM based adaptive learning method for text classification from positive and unlabeled documents. Knowl. Inf. Syst. 16(3), 281\u2013301 (2008)","journal-title":"Knowl. Inf. Syst."},{"key":"47_CR26","unstructured":"Plessis, M.D., Niu, G., Sugiyama, M.: Convex formulation for learning from positive and unlabeled data. In: Proceedings of Machine Learning Research, vol. 37, pp. 1386\u20131394. PMLR, Lille, France (Jul 2015). http:\/\/proceedings.mlr.press\/v37\/plessis15.html"},{"key":"47_CR27","unstructured":"Ritchie, D., Thomas, A., Hanrahan, P., Goodman, N.: Neurally-guided procedural models: amortized inference for procedural graphics programs using neural networks. In: Lee, D., Sugiyama, M., Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 29, pp. 622\u2013630. Curran Associates, Inc. (2016). https:\/\/proceedings.neurips.cc\/paper\/2016\/file\/40008b9a5380fcacce3976bf7c08af5b-Paper.pdf"},{"key":"47_CR28","unstructured":"Shan, F., et al.: Lung infection quantification of covid-19 in CT images with deep learning. arXiv preprint arXiv:2003.04655 (2020)"},{"key":"47_CR29","doi-asserted-by":"crossref","unstructured":"Shi, F., et al.: Large-scale screening of covid-19 from community acquired pneumonia using infection size-aware classification. arXiv preprint arXiv:2003.09860 (2020)","DOI":"10.1088\/1361-6560\/abe838"},{"key":"47_CR30","doi-asserted-by":"publisher","unstructured":"Wang, S., et al.: A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). Eur. Radiol. 1\u20139 (2021). https:\/\/doi.org\/10.1007\/s00330-021-07715-1","DOI":"10.1007\/s00330-021-07715-1"},{"key":"47_CR31","unstructured":"WHO: Pulse survey on continuity of essential health services during the covid-19 pandemic (2020)"},{"key":"47_CR32","unstructured":"Zhao, J., Zhang, Y., He, X., Xie, P.: Covid-CT-dataset: a CT scan dataset about covid-19. arXiv preprint arXiv:2003.13865 (2020)"},{"key":"47_CR33","unstructured":"Zhu, C., Liu, B., Yu, Q., Liu, X., Yu, W.: A spy positive and unlabeled learning classifier and its application in HR SAR image scene interpretation. In: 2012 IEEE Radar Conference, pp. 0516\u20130521. IEEE (2012)"}],"container-title":["Lecture Notes in Computer Science","Information Processing in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-78191-0_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,20]],"date-time":"2021-06-20T06:07:41Z","timestamp":1624169261000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-78191-0_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030781903","9783030781910"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-78191-0_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"14 June 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IPMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Processing in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ipmi2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ipmi2021.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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"200","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":"59","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)"}}]}}