{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T08:32:55Z","timestamp":1782203575198,"version":"3.54.5"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030248994","type":"print"},{"value":"9783030249007","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","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":[[2019]]},"DOI":"10.1007\/978-3-030-24900-7_17","type":"book-chapter","created":{"date-parts":[[2019,7,10]],"date-time":"2019-07-10T08:12:15Z","timestamp":1562746335000},"page":"207-217","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["CP-MCNN: Multi-label Chest X-ray Diagnostic Based on Confidence Predictor and CNN"],"prefix":"10.1007","author":[{"given":"Huazhen","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junlong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sisi","family":"Lai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nengguang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jixiang","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,7,11]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Balasubramanian, V., Ho, S.S., Vovk, V.: Conformal prediction for reliable machine learning: theory, adaptations and applications (2014)","DOI":"10.1016\/B978-0-12-398537-8.00009-2"},{"issue":"8","key":"17_CR2","doi-asserted-by":"publisher","first-page":"0815003","DOI":"10.3788\/AOS201737.0815003","volume":"37","author":"L Gao","year":"2017","unstructured":"Gao, L., Wang, J., Fan, Y., Chen, N.: Robust visual tracking based on convolutional neural networks and conformal predictor. Acta Optica Sinica 37(8), 0815003 (2017)","journal-title":"Acta Optica Sinica"},{"issue":"9","key":"17_CR3","doi-asserted-by":"publisher","first-page":"3084","DOI":"10.1016\/j.patcog.2012.03.004","volume":"45","author":"G Madjarov","year":"2012","unstructured":"Madjarov, G., Kocev, D., Gjorgjevikj, D., D\u017eeroski, S.: An extensive experimental comparison of methods for multi-label learning. Pattern Recogn. 45(9), 3084\u20133104 (2012)","journal-title":"Pattern Recogn."},{"issue":"2","key":"17_CR4","first-page":"318","volume":"30","author":"Z Li","year":"2018","unstructured":"Li, Z., Zheng, Y., Zhang, C., Shi, Z.: Combining deep feature and multi-label classification for semantic image annotation. J. Comput.-Aided Des. Comput. Graph. 30(2), 318 (2018)","journal-title":"J. Comput.-Aided Des. Comput. Graph."},{"key":"17_CR5","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/978-3-319-65924-4_14","volume-title":"Quantification of Biophysical Parameters in Medical Imaging","author":"M Dewey","year":"2018","unstructured":"Dewey, M., Kachelrie\u00df, M.: Fundamentals of X-ray computed tomography: acquisition and reconstruction. In: Sack, I., Schaeffter, T. (eds.) Quantification of Biophysical Parameters in Medical Imaging, pp. 325\u2013339. Springer, Cham (2018). \n                      https:\/\/doi.org\/10.1007\/978-3-319-65924-4_14"},{"key":"17_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-3-319-59876-5_3","volume-title":"Image Analysis and Recognition","author":"J Mojoo","year":"2017","unstructured":"Mojoo, J., Kurosawa, K., Kurita, T.: Deep CNN with graph laplacian regularization for multi-label image annotation. In: Karray, F., Campilho, A., Cheriet, F. (eds.) ICIAR 2017. LNCS, vol. 10317, pp. 19\u201326. Springer, Cham (2017). \n                      https:\/\/doi.org\/10.1007\/978-3-319-59876-5_3"},{"key":"17_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/4168538","volume":"2018","author":"Rahib H. Abiyev","year":"2018","unstructured":"Abiyev, R.H., Ma\u2019aitah, M.K.S.: Deep convolutional neural networks for chest diseases detection (2018)","journal-title":"Journal of Healthcare Engineering"},{"key":"17_CR8","unstructured":"Rajpurkar, P., et al.: CheXNet: radiologist-level pneumonia detection on chest x-rays with deep learning 1711, 1 November 2017. \n                      http:\/\/adsabs.harvard.edu\/abs\/2017arXiv171105225R"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Schmidhuber, J.: Deep learning in neural networks: an overview (2014)","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"17_CR10","unstructured":"Shafer, G., Vovk, V.: A tutorial on conformal prediction. J. Mach. Learn. Res. 9, 371\u2013421 (2008). \n                      \n                        \n                      \n                      $$<$$\n                    Go to ISI\n                      \n                        \n                      \n                      $$>$$\n                    :\/\/000256642000002"},{"key":"17_CR11","unstructured":"Iwata, T., Ghahramani, Z.: Improving output uncertainty estimation and generalization in deep learning via neural network Gaussian processes (2017)"},{"issue":"3","key":"17_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/jdwm.2007070101","volume":"3","author":"G Tsoumakas","year":"2007","unstructured":"Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehouse. Mining (IJDWM) 3(3), 1\u201313 (2007)","journal-title":"Int. J. Data Warehouse. Mining (IJDWM)"},{"key":"17_CR13","doi-asserted-by":"publisher","DOI":"10.1007\/b106715","volume-title":"Algorithmic Learning in a Random World","author":"V Vovk","year":"2005","unstructured":"Vovk, V., Gammerman, A., Shafer, G.: Algorithmic Learning in a Random World. Springer, New York (2005). \n                      https:\/\/doi.org\/10.1007\/b106715"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: Chestx-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3462\u20133471 (2017)","DOI":"10.1109\/CVPR.2017.369"},{"key":"17_CR15","unstructured":"Zhang, M., Zhou, Z.: A review on multi-label learning algorithms (2013)"},{"key":"17_CR16","unstructured":"Zhao, H.J.W.C.C.: Pulmonary tuberculosis detection model of chest x-ray images using convolutional neural network, 8 July 2018"},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Zhu, J., Liao, S., Yi, D., Lei, Z., Li, S.Z.: Multi-label CNN based pedestrian attribute learning for soft biometrics. In: International Conference on Biometrics, pp. 535\u2013540 (2015)","DOI":"10.1109\/ICB.2015.7139070"}],"container-title":["Lecture Notes in Computer Science","Security, Privacy, and Anonymity in Computation, Communication, and Storage"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-24900-7_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,7,10]],"date-time":"2019-07-10T08:23:49Z","timestamp":1562747029000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-24900-7_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030248994","9783030249007"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-24900-7_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"11 July 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SpaCCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Atlanta, GA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 July 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"spaccs2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/cse.stfx.ca\/~cybermatics\/2019\/spaccs\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}