{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T15:56:37Z","timestamp":1778255797731,"version":"3.51.4"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030872366","type":"print"},{"value":"9783030872373","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-87237-3_9","type":"book-chapter","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T06:19:41Z","timestamp":1632377981000},"page":"87-96","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["I-SECRET: Importance-Guided Fundus Image Enhancement via Semi-supervised Contrastive Constraining"],"prefix":"10.1007","author":[{"given":"Pujin","family":"Cheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyan","family":"Lyu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoying","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Bhattacharjee, D., Kim, S., Vizier, G., Salzmann, M.: DUNIT: detection-based unsupervised image-to-image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 4787\u20134796 (2020)","DOI":"10.1109\/CVPR42600.2020.00484"},{"issue":"11","key":"9_CR2","doi-asserted-by":"publisher","first-page":"2536","DOI":"10.1109\/TMI.2018.2838550","volume":"37","author":"J Cheng","year":"2020","unstructured":"Cheng, J., et al.: Structure-preserving guided retinal image filtering and its application for optic disk analysis. IEEE Trans. Med. Imaging TMI 37(11), 2536\u20132546 (2020)","journal-title":"IEEE Trans. Med. Imaging TMI"},{"key":"9_CR3","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"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: Proceedings of 2010 20th International Conference on Pattern Recognition, pp. 2366\u20132369 (2010)","DOI":"10.1109\/ICPR.2010.579"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Huang, Y., Lin, L., Li, M., Wu, J., et al.: Automated hemorrhage detection from coarsely annotated fundus images in diabetic retinopathy. In: Proceedings of the IEEE 17th International Symposium on Biomedical Imaging, ISBI, pp. 1369\u20131372 (2020)","DOI":"10.1109\/ISBI45749.2020.9098319"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Li, L., Verma, M., Nakashima, Y., Nagahara, H., Kawasaki, R.: IterNet: retinal image segmentation utilizing structural redundancy in vessel networks. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, WACV, pp. 3656\u20133665 (2020)","DOI":"10.1109\/WACV45572.2020.9093621"},{"issue":"1","key":"9_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-019-0340-y","volume":"7","author":"L Lin","year":"2020","unstructured":"Lin, L., Li, M., Huang, Y., Cheng, P., Xia, H., et al.: The SUSTech-SYSU dataset for automated exudate detection and diabetic retinopathy grading. Sci. Data 7(1), 1\u20131 (2020)","journal-title":"Sci. Data"},{"key":"9_CR9","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 Conference on Computer Vision and Pattern Recognition, CVPR, pp. 2794\u20132802 (2017)","DOI":"10.1109\/ICCV.2017.304"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Mathew, S., Nadeem, S., Kumari, S., Kaufman, A.: Augmenting colonoscopy using extended and directional CycleGAN for lossy image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 4696\u20134705 (2020)","DOI":"10.1109\/CVPR42600.2020.00475"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Nizan, O., Tal, A.: Breaking the cycle-colleagues are all you need. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 7860\u20137869 (2020)","DOI":"10.1109\/CVPR42600.2020.00788"},{"key":"9_CR12","unstructured":"Oord, A.V.D., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748"},{"issue":"Jan","key":"9_CR13","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.cmpb.2017.10.017","volume":"153","author":"JI Orlando","year":"2018","unstructured":"Orlando, J.I., Prokofyeva, E., Del Fresno, M., Blaschko, M.B.: An ensemble deep learning based approach for red lesion detection in fundus images. Comput. Meth. Prog. Biomed. 153(Jan), 115\u2013127 (2018)","journal-title":"Comput. Meth. Prog. Biomed."},{"key":"9_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1007\/978-3-030-58545-7_19","volume-title":"Computer Vision \u2013 ECCV 2020","author":"T Park","year":"2020","unstructured":"Park, T., Efros, A.A., Zhang, R., Zhu, J.-Y.: Contrastive learning for unpaired image-to-image translation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 319\u2013345. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58545-7_19"},{"key":"9_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/978-3-030-63419-3_19","volume-title":"Ophthalmic Medical Image Analysis","author":"AD P\u00e9rez","year":"2020","unstructured":"P\u00e9rez, A.D., Perdomo, O., Rios, H., Rodr\u00edguez, F., Gonz\u00e1lez, F.A.: A conditional generative adversarial network-based method for eye fundus image quality enhancement. In: Fu, H., Garvin, M.K., MacGillivray, T., Xu, Y., Zheng, Y. (eds.) OMIA 2020. LNCS, vol. 12069, pp. 185\u2013194. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-63419-3_19"},{"issue":"8","key":"9_CR16","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1049\/iet-ipr.2018.6212","volume":"13","author":"A Raj","year":"2019","unstructured":"Raj, A., Tiwari, A.K., Martini, M.G.: Fundus image quality assessment: survey, challenges, and future scope. IET Image Process. 13(8), 1211\u20131224 (2019)","journal-title":"IET Image Process."},{"key":"9_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1007\/978-3-030-63419-3_4","volume-title":"Ophthalmic Medical Image Analysis","author":"S Sengupta","year":"2020","unstructured":"Sengupta, S., Wong, A., Singh, A., Zelek, J., Lakshminarayanan, V.: DeSupGAN: multi-scale feature averaging generative adversarial network for simultaneous de-blurring and super-resolution of retinal fundus images. In: Fu, H., Garvin, M.K., MacGillivray, T., Xu, Y., Zheng, Y. (eds.) OMIA 2020. LNCS, vol. 12069, pp. 32\u201341. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-63419-3_4"},{"issue":"4","key":"9_CR18","doi-asserted-by":"publisher","first-page":"046006","DOI":"10.1117\/1.JBO.19.4.046006","volume":"19","author":"U Sevik","year":"2014","unstructured":"Sevik, U., Kose, C., Berber, T., Erdol, H.: Identification of suitable fundus images using automated quality assessment methods. J. Biomed. Opt. 19(4), 046006 (2014)","journal-title":"J. Biomed. Opt."},{"issue":"3","key":"9_CR19","doi-asserted-by":"publisher","first-page":"996","DOI":"10.1109\/TMI.2020.3043495","volume":"40","author":"Z Shen","year":"2020","unstructured":"Shen, Z., Fu, H., Shen, J.: Modeling and enhancing low-quality retinal fundus images. IEEE Trans. Med. Imaging TMI 40(3), 996\u20131006 (2020)","journal-title":"IEEE Trans. Med. Imaging TMI"},{"issue":"4","key":"9_CR20","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/TMI.2004.825627","volume":"23","author":"J Staal","year":"2004","unstructured":"Staal, J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging TMI 23(4), 501\u2013509 (2004). https:\/\/doi.org\/10.1109\/TMI.2004.825627","journal-title":"IEEE Trans. Med. Imaging TMI"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Wang, Y., Khan, S., Gonzalez-Garcia, A., Weijer, J.V.D., Khan, F.S.: Semi-supervised learning for few-shot image-to-image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 4453\u20134462 (2020)","DOI":"10.1109\/CVPR42600.2020.00451"},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"You, Q., Wan, C., Sun, J., Shen, J., Ye, H., Yu, Q.: Fundus image enhancement method based on CycleGAN. In: Proceedings of 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, pp. 4500\u20134503 (2019)","DOI":"10.1109\/EMBC.2019.8856950"},{"key":"9_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1007\/978-3-030-32239-7_9","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"H Zhao","year":"2019","unstructured":"Zhao, H., Yang, B., Cao, L., Li, H.: Data-driven enhancement of blurry retinal images via generative adversarial networks. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 75\u201383. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_9"},{"key":"9_CR24","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, ICCV, pp. 2223\u20132232 (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"9_CR25","unstructured":"Zhuang, J., et al.: AdaBelief optimizer: adapting stepsizes by the belief in observed gradients. arXiv preprint arXiv:2010.07468 (2020)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87237-3_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T07:00:55Z","timestamp":1632380455000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87237-3_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872366","9783030872373"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87237-3_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.org\/en\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1622","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":"531","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":"33% - 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)"}},{"value":"The conference was held virtually.","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)"}}]}}