{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T11:58:34Z","timestamp":1790251114737,"version":"4.1.0"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030326913","type":"print"},{"value":"9783030326920","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":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-32692-0_18","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T12:04:21Z","timestamp":1570622661000},"page":"151-159","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Semi-supervised Multi-task Learning with Chest X-Ray Images"],"prefix":"10.1007","author":[{"given":"Abdullah-Al-Zubaer","family":"Imran","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Demetri","family":"Terzopoulos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Caruana, R.: Multitask learning: a knowledge-based source of inductive bias. In: International Conference on Machine Learning (1993)","DOI":"10.1016\/B978-1-55860-307-3.50012-5"},{"key":"18_CR2","first-page":"511","volume":"101","author":"A Mittal","year":"2018","unstructured":"Mittal, A., Hooda, R., Sofat, S.: LF-SegNet: a fully convolutional encoder-decoder network for segmenting lung fields from chest radiographs. WPC 101, 511\u2013529 (2018)","journal-title":"WPC"},{"key":"18_CR3","doi-asserted-by":"crossref","unstructured":"Zhu, W., Xiang, X., Tran, T.D., Hager, G.D., Xie, X.: Adversarial deep structured nets for mass segmentation from mammograms. In: ISBI (2018)","DOI":"10.1109\/ISBI.2018.8363704"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Chen, C., Dou, Q., et al.: Semantic-aware generative adversarial nets for unsupervised domain adaptation in chest X-ray segment. arXiv:1806.00600 (2018)","DOI":"10.1007\/978-3-030-00919-9_17"},{"key":"18_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/978-3-030-00889-5_30","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"W Dai","year":"2018","unstructured":"Dai, W., Dong, N., Wang, Z., Liang, X., Zhang, H., Xing, E.P.: SCAN: structure correcting adversarial network for organ segmentation in chest X-rays. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 263\u2013273. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_30"},{"key":"18_CR6","doi-asserted-by":"crossref","unstructured":"Madani, A., et al.: Semi-supervised learning with GANs for chest X-ray classification with ability of data domain adaptation. In: Proceedings of ISBI (2018)","DOI":"10.1109\/ISBI.2018.8363749"},{"key":"18_CR7","unstructured":"Guan, Q., Huang, Y., et al.: Diagnose like a radiologist: attention guided convolutional net for thorax disease classification. arXiv:1801.09927 (2018)"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Imran, A.A.Z., Terzopoulos, D.: Multi-adversarial variational autoencoder networks. arXiv preprint arXiv:1906.06430 (2019)","DOI":"10.1109\/ICMLA.2019.00137"},{"key":"18_CR9","unstructured":"Rezaei, M., Yang, H., et al.: Multi-task generative adversarial network for handling imbalanced clinical data. CoRR (2018)"},{"key":"18_CR10","first-page":"96","volume":"94","author":"F Girard","year":"2019","unstructured":"Girard, F., Kavalec, C., Cheriet, F.: Joint segmentation and classification of retinal arteries\/veins from fundus images. AI Med. 94, 96\u2013109 (2019)","journal-title":"AI Med."},{"key":"18_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1007\/978-3-030-00934-2_99","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"S Mehta","year":"2018","unstructured":"Mehta, S., Mercan, E., Bartlett, J., Weaver, D., Elmore, J.G., Shapiro, L.: Y-Net: joint segmentation and classification for diagnosis of breast biopsy images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 893\u2013901. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_99"},{"key":"18_CR12","unstructured":"Yang, X., Zeng, Z., Yeo, S.Y., et al.: A novel multi-task deep learning model for skin lesion segmentation and classification. arXiv:1703.01025 (2017)"},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Khosravan, N., Bagci, U.: Semi-supervised multi-task learning for lung cancer diagnosis. In: EMBC (2018)","DOI":"10.1109\/EMBC.2018.8512294"},{"key":"18_CR14","first-page":"1597","volume":"37","author":"H Fu","year":"2018","unstructured":"Fu, H., Cheng, J., Xu, Y., et al.: Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE TMI 37, 1597\u20131605 (2018)","journal-title":"IEEE TMI"},{"key":"18_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1007\/978-3-030-00889-5_32","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"A-A-Z Imran","year":"2018","unstructured":"Imran, A.-A.-Z., Hatamizadeh, A., Ananth, S.P., Ding, X., Terzopoulos, D., Tajbakhsh, N.: Automatic segmentation of pulmonary lobes using a progressive dense V-network. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS 2018. LNCS, vol. 11045, pp. 282\u2013290. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_32"},{"key":"18_CR16","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., et al.: Attention U-net: learning where to look for the pancreas. arXiv:1804.03999 (2018)"},{"key":"18_CR17","first-page":"475","volume":"4","author":"S Jaeger","year":"2014","unstructured":"Jaeger, S., Candemir, S., et al.: Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant. Imaging Med. Surg. 4, 475 (2014)","journal-title":"Quant. Imaging Med. Surg."},{"key":"18_CR18","doi-asserted-by":"publisher","first-page":"71","DOI":"10.2214\/ajr.174.1.1740071","volume":"174","author":"J Shiraishi","year":"2000","unstructured":"Shiraishi, J., Katsuragawa, S., et al.: Development of a digital image database for chest radiographs with and without a lung nodule. J. Roentgenol. 174, 71\u201374 (2000)","journal-title":"J. Roentgenol."}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32692-0_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:03:02Z","timestamp":1728432182000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32692-0_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030326913","9783030326920"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32692-0_18","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":"10 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"13 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/mlmi2019.web.unc.edu\/","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":"158","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":"78","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":"49% - 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":"2","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}