{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T16:24:48Z","timestamp":1792513488820,"version":"4.5.0"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030598600","type":"print"},{"value":"9783030598617","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-59861-7_53","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T03:07:37Z","timestamp":1601608057000},"page":"523-531","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Mammographic Image Conversion Between Source and Target Acquisition Systems Using cGAN"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9146-7292","authenticated-orcid":false,"given":"Zahra","family":"Ghanian","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1655-4177","authenticated-orcid":false,"given":"Andreu","family":"Badal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3847-7448","authenticated-orcid":false,"given":"Kenny","family":"Cha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3833-6696","authenticated-orcid":false,"given":"Mohammad Mehdi","family":"Farhangi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5167-8899","authenticated-orcid":false,"given":"Nicholas","family":"Petrick","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2804-2264","authenticated-orcid":false,"given":"Berkman","family":"Sahiner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"53_CR1","unstructured":"Denton, E., Chintala, S., Fergus, R.: Deep generative image models using a Laplacian pyramid of adversarial networks. In: Advances in Neural Information Processing Systems, pp. 1486\u20131494 (2015)"},{"key":"53_CR2","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)"},{"key":"53_CR3","doi-asserted-by":"crossref","unstructured":"Ledig, C., et al.: Photo-realistic single image superresolution using a generative adversarial network. arXiv preprint (2016)","DOI":"10.1109\/CVPR.2017.19"},{"key":"53_CR4","unstructured":"Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)"},{"key":"53_CR5","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J., Zhou, T., Efros, A.: Image-to-image translation with conditional adversarial networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"issue":"4","key":"53_CR6","doi-asserted-by":"publisher","first-page":"044502","DOI":"10.1117\/1.JMI.5.4.044502","volume":"5","author":"Z Ghanian","year":"2018","unstructured":"Ghanian, Z., Pezeshk, A., Petrick, N., Sahiner, B.: Computational insertion of microcalcification clusters on mammograms: reader differentiation from native clusters and computer-aided detection comparison. J. Med. Imaging 5(4), 044502 (2018)","journal-title":"J. Med. Imaging"},{"issue":"7","key":"53_CR7","doi-asserted-by":"publisher","first-page":"e185474","DOI":"10.1001\/jamanetworkopen.2018.5474","volume":"1","author":"A Badano","year":"2018","unstructured":"Badano, A., Graff, C.G., Badal, A.: Evaluation of digital breast tomosynthesis as replacement of fullfield digital mammography using an in-silico imaging trial. JAMA Netw. Open 1(7), e185474 (2018)","journal-title":"JAMA Netw. Open"},{"key":"53_CR8","doi-asserted-by":"publisher","first-page":"4878","DOI":"10.1118\/1.3231824","volume":"36","author":"A Badal","year":"2009","unstructured":"Badal, A., Badano, A.: Accelerating Monte Carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit. Med. Phys. 36, 4878\u20134880 (2009)","journal-title":"Med. Phys."},{"key":"53_CR9","doi-asserted-by":"publisher","unstructured":"Jin, D., Xu, Z., Tang, Y., Harrison, A.P., Mollura, D.J.: CT-Realistic lung nodule simulation from 3d conditional\ngenerative adversarial networks for robust lung segmentation. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018.\nLNCS, vol. 11071, pp 732\u2013740. Springer, Cham.https:\/\/doi.org\/10.1007\/978-3-030-00934-2_81","DOI":"10.1007\/978-3-030-00934-2_81"},{"key":"53_CR10","doi-asserted-by":"crossref","unstructured":"Pathak, D., Krahenbuhl, P., Donahue, J.: Context encoders: feature learning by inpainting. In: Proceedings of the IEEE CVPR, pp. 2536\u20132544 (2016)","DOI":"10.1109\/CVPR.2016.278"},{"key":"53_CR11","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"53_CR12","doi-asserted-by":"publisher","first-page":"3006","DOI":"10.1118\/1.1621870","volume":"30","author":"R Saunders","year":"2003","unstructured":"Saunders, R., Samei, E.: A method for modifying the image quality parameters of digital radiographic images. Med. Phys. 30, 3006\u20133017 (2003)","journal-title":"Med. Phys."}],"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\/978-3-030-59861-7_53","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:04:34Z","timestamp":1759356274000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59861-7_53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030598600","9783030598617"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59861-7_53","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","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":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mlmi2020.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":"101","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":"68","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":"67% - 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":"2.04","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":"3.43","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":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}