{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T14:52:53Z","timestamp":1777733573494,"version":"3.51.4"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031340475","type":"print"},{"value":"9783031340482","type":"electronic"}],"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\/978-3-031-34048-2_32","type":"book-chapter","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T12:03:35Z","timestamp":1686139415000},"page":"415-427","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by\u00a0Enhancing"],"prefix":"10.1007","author":[{"given":"Wenhui","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peijie","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oana M.","family":"Dumitrascu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jacob M.","family":"Sobczak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Farazi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhangsihao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keshav","family":"Nandakumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yalin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,8]]},"reference":[{"issue":"10","key":"32_CR1","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1001\/jamaophthalmol.2020.3190","volume":"138","author":"RM Wolf","year":"2020","unstructured":"Wolf, R.M., Channa, R., Abramoff, M.D., Lehmann, H.P.: Cost-effectiveness of autonomous point-of-care diabetic retinopathy screening for pediatric patients with diabetes. JAMA Ophthalmol. 138(10), 1063\u20131069 (2020)","journal-title":"JAMA Ophthalmol."},{"issue":"11","key":"32_CR2","doi-asserted-by":"publisher","first-page":"e806","DOI":"10.1016\/S2589-7500(22)00169-8","volume":"4","author":"CY Cheung","year":"2022","unstructured":"Cheung, C.Y., et al.: A deep learning model for detection of Alzheimer\u2019s disease based on retinal photographs: a retrospective, multicentre case-control study. Lancet Digit. Health 4(11), e806\u2013e815 (2022)","journal-title":"Lancet Digit. Health"},{"issue":"3","key":"32_CR3","doi-asserted-by":"publisher","first-page":"996","DOI":"10.1109\/TMI.2020.3043495","volume":"40","author":"Z Shen","year":"2021","unstructured":"Shen, Z., Fu, H., Shen, J., Shao, L.: Modeling and enhancing low-quality retinal fundus images. IEEE Trans. Med. Imaging 40(3), 996\u20131006 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"32_CR4","first-page":"2965","volume":"80","author":"J Lehtinen","year":"2018","unstructured":"Lehtinen, J., et al.: Noise2Noise: learning image restoration without clean data. ICML 80, 2965\u20132974 (2018)","journal-title":"ICML"},{"key":"32_CR5","doi-asserted-by":"crossref","unstructured":"Krull, A., et al.: Noise2void-learning denoising from single noisy images. In: Proceedings of the IEEE Computer Society Conference Computer Vision and Pattern Recognition, pp. 2129\u20132137 (2019)","DOI":"10.1109\/CVPR.2019.00223"},{"key":"32_CR6","doi-asserted-by":"crossref","unstructured":"Bousmalis, K., Silberman, N., et al.: Unsupervised pixel-level domain adaptation with generative adversarial networks. In: CVPR (2016)","DOI":"10.1109\/CVPR.2017.18"},{"key":"32_CR7","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"32_CR8","doi-asserted-by":"crossref","unstructured":"Zhu, J., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: CVPR, pp. 2242\u20132251 (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"32_CR9","unstructured":"Liu, M.Y., Tuzel, O.: Coupled generative adversarial networks. In: Advances in Neural Information Processing Systems (2016)"},{"key":"32_CR10","unstructured":"Wang, W., Wen, F., Yan, Z., Liu, P.: Optimal transport for unsupervised denoising learning. IEEE PAMI 1 (2022)"},{"issue":"4","key":"32_CR11","doi-asserted-by":"publisher","first-page":"1804","DOI":"10.1137\/16M1102884","volume":"10","author":"Y Romano","year":"2017","unstructured":"Romano, Y., Elad, M., Milanfar, P.: The little engine that could: regularization by denoising (RED). SIAM J. Imag. Sci. 10(4), 1804\u20131844 (2017)","journal-title":"SIAM J. Imag. Sci."},{"key":"32_CR12","first-page":"5546","volume":"97","author":"E Ryu","year":"2019","unstructured":"Ryu, E., Liu, J., Wang, S., Chen, X., Wang, Z., Yin, W.: Plug-and-play methods provably converge with properly trained denoisers. PMLR 97, 5546\u20135557 (2019)","journal-title":"PMLR"},{"issue":"1","key":"32_CR13","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/MSP.2017.2760358","volume":"35","author":"A Lucas","year":"2018","unstructured":"Lucas, A., Iliadis, M., Molina, R., Katsaggelos, A.K.: Using deep neural networks for inverse problems in imaging: beyond analytical methods. IEEE Signal Process. Mag. 35(1), 20\u201336 (2018)","journal-title":"IEEE Signal Process. Mag."},{"key":"32_CR14","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: ECA-net: efficient channel attention for deep convolutional neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"32_CR15","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: CVPR (2016)","DOI":"10.1109\/CVPR.2017.19"},{"key":"32_CR16","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., et al.: Improved training of wasserstein GANs. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"issue":"4","key":"32_CR17","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1109\/TIP.2011.2173206","volume":"21","author":"D Brunet","year":"2012","unstructured":"Brunet, D., Vrscay, E.R., Wang, Z.: On the mathematical properties of the structural similarity index. IEEE Trans. Image Process. 21(4), 1488\u20131499 (2012)","journal-title":"IEEE Trans. Image Process."},{"key":"32_CR18","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":"32_CR19","unstructured":"Miyato, T., et al.: Spectral normalization for generative adversarial networks. In: International Conference on Learning Representations (2018)"},{"key":"32_CR20","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"32_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1007\/978-3-030-32239-7_6","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"H Fu","year":"2019","unstructured":"Fu, H., et al.: Evaluation of retinal image quality assessment networks in different color-spaces. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 48\u201356. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_6"},{"issue":"4","key":"32_CR22","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/TMI.2004.825627","volume":"23","author":"J Staal","year":"2004","unstructured":"Staal, J., et al.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501\u2013509 (2004)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"32_CR23","doi-asserted-by":"crossref","unstructured":"Porwal, P., et al.: IDRID: a database for diabetic retinopathy screening research. Data 3(3) (2018)","DOI":"10.3390\/data3030025"},{"key":"32_CR24","doi-asserted-by":"crossref","unstructured":"Zhu, W., Qiu, P., Lepore, N., Dumitrascu, O., Wang, Y.: Self-supervised equivariant regularization reconciles multiple instance learning: joint referable diabetic retinopathy classification and lesion segmentation. In: 18th International Symposium on Medical Information Processing and Analysis (SIPAIM) (2022)","DOI":"10.1117\/12.2669772"}],"container-title":["Lecture Notes in Computer Science","Information Processing in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-34048-2_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T12:07:31Z","timestamp":1686139651000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34048-2_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031340475","9783031340482"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34048-2_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"8 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IPMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Processing in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"San Carlos de Bariloche","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Argentina","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":"12 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ipmi2023","order":10,"name":"conference_id","label":"Conference ID","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":"169","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":"63","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":"37% - 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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}