{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:12:46Z","timestamp":1743099166982,"version":"3.40.3"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030326913"},{"type":"electronic","value":"9783030326920"}],"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_19","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T12:04:21Z","timestamp":1570622661000},"page":"160-168","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Novel Bi-directional Images Synthesis Based on WGAN-GP with GMM-Based Noise Generation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0541-8612","authenticated-orcid":false,"given":"Wei","family":"Huang","sequence":"first","affiliation":[]},{"given":"Mingyuan","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Xi","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Huijun","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Ni","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"issue":"12","key":"19_CR1","first-page":"2598","volume":"35","author":"N Cordier","year":"2016","unstructured":"Cordier, N., Delingette, H., Le, M., Ayache, N.: Extended modality propagation: image synthesis of pathological cases. IEEE-TMI 35(12), 2598\u20132608 (2016)","journal-title":"IEEE-TMI"},{"issue":"3","key":"19_CR2","first-page":"815","volume":"37","author":"Y Huang","year":"2018","unstructured":"Huang, Y., et al.: Cross-modality image synthesis via weakly coupled and geometry co-regularized joint dictionary learning. IEEE-TMI 37(3), 815\u2013827 (2018)","journal-title":"IEEE-TMI"},{"issue":"3","key":"19_CR3","first-page":"703","volume":"37","author":"I Polycarpou","year":"2018","unstructured":"Polycarpou, I., et al.: Synthesis of realistic simultaneous positron emission tomography and magnetic resonance imaging data. IEEE-TMI 37(3), 703\u2013711 (2018)","journal-title":"IEEE-TMI"},{"issue":"3","key":"19_CR4","first-page":"741","volume":"37","author":"Y Zhou","year":"2018","unstructured":"Zhou, Y., Giffard-Roisin, S., De Craene, M., et al.: A framework for the generation of realistic synthetic cardiac ultrasound and magnetic resonance imaging sequences from the same virtual patients. IEEE-TMI 37(3), 741\u2013754 (2018)","journal-title":"IEEE-TMI"},{"issue":"3","key":"19_CR5","first-page":"781","volume":"37","author":"P Costa","year":"2018","unstructured":"Costa, P., Galdran, A., Meyer, M., et al.: End-to-end adversarial retinal image synthesis. IEEE-TMI 37(3), 781\u2013791 (2018)","journal-title":"IEEE-TMI"},{"key":"19_CR6","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2906677","author":"W Huang","year":"2019","unstructured":"Huang, W., et al.: Arterial spin labeling images synthesis from sMRI using unbalanced deep discriminant learning. IEEE-TMI (2019). https:\/\/doi.org\/10.1109\/TMI.2019.2906677","journal-title":"IEEE-TMI"},{"key":"19_CR7","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial networks. In: NIPS, Montreal, pp. 2672\u20132680 (2014)"},{"key":"19_CR8","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv arXiv:1701.07875 (2017)"},{"key":"19_CR9","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of Wasserstein GANs. arXiv arXiv:1704.00028 (2017)"},{"key":"19_CR10","unstructured":"Kingma, D., Dhariwal, P.: Glow: generative flow with invertible 1x1 convolutions. In: NIPS, Vancouver, pp. 10236\u201310245 (2018)"},{"key":"19_CR11","unstructured":"Dinh, L., Krueger, D., Bengio, Y.: NICE: non-linear independent components estimation. In: ICLR, San Diego (2015)"},{"key":"19_CR12","unstructured":"Dinh, L., Sohl-Dickstein, J., Bengio, S.: Density estimation using Real NVP. In ICLR, Toulon (2017)"}],"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_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:02:58Z","timestamp":1728432178000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32692-0_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030326913","9783030326920"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32692-0_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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"}]}}