{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:37:26Z","timestamp":1742920646930,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031390586"},{"type":"electronic","value":"9783031390593"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-39059-3_25","type":"book-chapter","created":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T13:01:37Z","timestamp":1690722097000},"page":"370-384","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Generative Adversarial Networks for\u00a0Domain Translation in\u00a0Unpaired Breast DCE-MRI Datasets"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9911-1517","authenticated-orcid":false,"given":"Antonio","family":"Galli","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5033-9617","authenticated-orcid":false,"given":"Michela","family":"Gravina","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6852-0377","authenticated-orcid":false,"given":"Stefano","family":"Marrone","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8176-6950","authenticated-orcid":false,"given":"Carlo","family":"Sansone","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"key":"25_CR1","doi-asserted-by":"publisher","first-page":"102169","DOI":"10.1016\/j.compmedimag.2022.102169","volume":"104","author":"N Cai","year":"2023","unstructured":"Cai, N., Chen, H., Li, Y., Peng, Y., Guo, L.: Registration on DCE-MRI images via multi-domain image-to-image translation. Comput. Med. Imaging Graph. 104, 102169 (2023)","journal-title":"Comput. Med. Imaging Graph."},{"key":"25_CR2","doi-asserted-by":"crossref","unstructured":"Desai, S.D., Giraddi, S., Verma, N., Gupta, P., Ramya, S.: Breast cancer detection using gan for limited labeled dataset. In: 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN), pp. 34\u201339. IEEE (2020)","DOI":"10.1109\/CICN49253.2020.9242551"},{"issue":"11","key":"25_CR3","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"25_CR4","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1007\/978-3-031-06427-2_9","volume-title":"Image Analysis and Processing-ICIAP 2022","author":"M Gravina","year":"2022","unstructured":"Gravina, M., et al.: Leveraging CycleGAN in lung CT Sinogram-free kernel conversion. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds.) ICIAP 2022 Part I. LNCS, vol. 13231, pp. 100\u2013110. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-06427-2_9"},{"key":"25_CR5","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. arXiv (2018). https:\/\/arxiv.org\/abs\/1611.07004","DOI":"10.1109\/CVPR.2017.632"},{"key":"25_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-319-46475-6_43","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Johnson","year":"2016","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694\u2013711. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43"},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2794\u20132802 (2017)","DOI":"10.1109\/ICCV.2017.304"},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Modanwal, G., Vellal, A., Mazurowski, M.A.: Normalization of breast MRIs using cycle-consistent generative adversarial networks. arXiv (2019). https:\/\/arxiv.org\/abs\/1912.08061","DOI":"10.1117\/12.2551301"},{"key":"25_CR10","unstructured":"Murphy, A., Niknejad, D.M.T.: Fat suppressed imaging. https:\/\/radiopaedia.org\/articles\/fat-suppressed-imaging?lang=us"},{"key":"25_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"25_CR12","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/978-3-031-06427-2_3","volume-title":"Image Analysis and Processing-ICIAP 2022","author":"C Sannino","year":"2022","unstructured":"Sannino, C., Gravina, M., Marrone, S., Fiameni, G., Sansone, C.: Lessonable: leveraging deep fakes in MOOC content creation. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds.) ICIAP 2022 Part I. LNCS, vol. 13231, pp. 27\u201337. Springer, Cham (2022)"},{"key":"25_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-021-01488-9","volume":"21","author":"S Secinaro","year":"2021","unstructured":"Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., Biancone, P.: The role of artificial intelligence in healthcare: a structured literature review. BMC Med. Inform. Decis. Mak. 21, 1\u201323 (2021)","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"25_CR14","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.inffus.2021.02.014","volume":"72","author":"P Shamsolmoali","year":"2021","unstructured":"Shamsolmoali, P., Zareapoor, M., Granger, E., Zhou, H., Wang, R., Celebi, M.E., Yang, J.: Image synthesis with adversarial networks: a comprehensive survey and case studies. Inf. Fusion 72, 126\u2013146 (2021)","journal-title":"Inf. Fusion"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., Webb, R.: Learning from simulated and unsupervised images through adversarial training. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2107\u20132116 (2017)","DOI":"10.1109\/CVPR.2017.241"},{"issue":"12","key":"25_CR16","doi-asserted-by":"publisher","first-page":"351","DOI":"10.3390\/fi14120351","volume":"14","author":"S Tavse","year":"2022","unstructured":"Tavse, S., Varadarajan, V., Bachute, M., Gite, S., Kotecha, K.: A systematic literature review on applications of GAN-synthesized images for brain MRI. Future Internet 14(12), 351 (2022)","journal-title":"Future Internet"},{"key":"25_CR17","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: the missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016)"},{"key":"25_CR18","unstructured":"Wolf, S.: Cyclegan: Learning to translate images (without paired training data) (2018). https:\/\/towardsdatascience.com\/cyclegan-learning-to-translate-images-without-paired-training-data-5b4e93862c8d"},{"key":"25_CR19","doi-asserted-by":"crossref","unstructured":"Wolterink, J.M., Dinkla, A.M., Savenije, M.H., Seevinck, P.R., van den Berg, C.A., Isgum, I.: Deep MR to CT synthesis using unpaired data. arXiv (2017). https:\/\/arxiv.org\/abs\/1708.01155","DOI":"10.1007\/978-3-319-68127-6_2"},{"key":"25_CR20","doi-asserted-by":"crossref","unstructured":"Xie, G., et al.: Fedmed-gan: Federated domain translation on unsupervised cross-modality brain image synthesis (2022). Available at SSRN 4342071","DOI":"10.2139\/ssrn.4342071"},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223\u20132232 (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"25_CR22","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv (2020). https:\/\/arxiv.org\/abs\/1703.10593"}],"container-title":["Communications in Computer and Information Science","Deep Learning Theory and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-39059-3_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T13:06:05Z","timestamp":1690722365000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-39059-3_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031390586","9783031390593"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-39059-3_25","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DeLTA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Deep Learning Theory and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rome","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"delta2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/delta.scitevents.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":"PRIMORIS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42","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":"9","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":"22","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":"21% - 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","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)"}}]}}