{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T21:26:28Z","timestamp":1769549188459,"version":"3.49.0"},"publisher-location":"Cham","reference-count":10,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030322472","type":"print"},{"value":"9783030322489","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\/978-3-030-32248-9_18","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:08:49Z","timestamp":1570662529000},"page":"155-163","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["CoCa-GAN: Common-Feature-Learning-Based Context-Aware Generative Adversarial Network for Glioma Grading"],"prefix":"10.1007","author":[{"given":"Pu","family":"Huang","sequence":"first","affiliation":[]},{"given":"Dengwang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zhicheng","family":"Jiao","sequence":"additional","affiliation":[]},{"given":"Dongming","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Guoshi","family":"Li","sequence":"additional","affiliation":[]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Han","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"key":"18_CR1","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.1002\/jmri.26704","volume":"50","author":"A Sengupta","year":"2019","unstructured":"Sengupta, A., Ramaniharan, A.K., Gupta, R.K., Agarwal, S., Singh, A.: Glioma grading using a machine-learning framework based on optimized features obtained from T1 perfusion mri and volumes of tumor components. J. Magn. Reson. Imaging 50, 1295\u20131306 (2019)","journal-title":"J. Magn. Reson. Imaging"},{"key":"18_CR2","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (2014)"},{"key":"18_CR3","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1016\/j.neuroimage.2018.03.045","volume":"174","author":"Y Wang","year":"2018","unstructured":"Wang, Y., Yu, B., Wang, L., et al.: 3D conditional generative adversarial networks for high-quality PET image estimation at low dose. Neuroimage 174, 550\u2013562 (2018)","journal-title":"Neuroimage"},{"issue":"12","key":"18_CR4","doi-asserted-by":"publisher","first-page":"2720","DOI":"10.1109\/TBME.2018.2814538","volume":"65","author":"D Nie","year":"2018","unstructured":"Nie, D., Trullo, R., Lian, J., et al.: Medical image synthesis with deep convolutional adversarial networks. IEEE Trans. Biomed. Eng. 65(12), 2720\u20132730 (2018)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"18_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/978-3-030-00931-1_52","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"Y Pan","year":"2018","unstructured":"Pan, Y., Liu, M., Lian, C., Zhou, T., Xia, Y., Shen, D.: Synthesizing missing PET from MRI with cycle-consistent generative adversarial networks for alzheimer\u2019s disease diagnosis. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 455\u2013463. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00931-1_52"},{"issue":"3","key":"18_CR6","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1109\/TMI.2017.2764326","volume":"37","author":"A Chartsias","year":"2018","unstructured":"Chartsias, A., Joyce, T., Giuffrida, M.V., Tsaftaris, S.A.: Multimodal MR synthesis via modality-invariant latent representation. IEEE Trans. Med. Imaging 37(3), 803\u2013814 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7","key":"18_CR7","doi-asserted-by":"publisher","first-page":"1750","DOI":"10.1109\/TMI.2019.2895894","volume":"38","author":"B Yu","year":"2019","unstructured":"Yu, B., Zhou, L., Wang, L., Shi, Y., Fripp, J., Bourgeat, P.: Ea-GANs: edge-aware Generative Adversarial Networks for Cross-modality MR Image Synthesis. IEEE Trans. Med. Imaging 38(7), 1750\u20131762 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"18_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1007\/978-3-030-02628-8_12","volume-title":"Understanding and Interpreting Machine Learning in Medical Image Computing Applications","author":"S Pereira","year":"2018","unstructured":"Pereira, S., Meier, R., Alves, V., Reyes, M., Silva, C.A.: Automatic brain tumor grading from mri data using convolutional neural networks and quality assessment. In: Stoyanov, D., et al. (eds.) MLCN\/DLF\/IMIMIC -2018. LNCS, vol. 11038, pp. 106\u2013114. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-02628-8_12"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.74"},{"issue":"10","key":"18_CR10","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze, B.H., Jakab, A., Bauer, S., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2015)","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32248-9_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:22:06Z","timestamp":1728519726000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32248-9_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322472","9783030322489"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32248-9_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":"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":"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":"17 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2019.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":"1730","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":"539","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":"3.07","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":"6.31","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":"This content has been made available to all.","name":"free","label":"Free to read"}]}}