{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T15:27:15Z","timestamp":1772206035432,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030272715","type":"print"},{"value":"9783030272722","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-27272-2_29","type":"book-chapter","created":{"date-parts":[[2019,8,11]],"date-time":"2019-08-11T23:02:59Z","timestamp":1565564579000},"page":"333-344","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Improving Lesion Segmentation for Diabetic Retinopathy Using Adversarial Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9695-0603","authenticated-orcid":false,"given":"Qiqi","family":"Xiao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4861-8072","authenticated-orcid":false,"given":"Jiaxu","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6273-0138","authenticated-orcid":false,"given":"Muqiao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1380-6620","authenticated-orcid":false,"given":"Alex","family":"Gaudio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9389-4060","authenticated-orcid":false,"given":"Kris","family":"Kitani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8524-997X","authenticated-orcid":false,"given":"Asim","family":"Smailagic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9528-1292","authenticated-orcid":false,"given":"Pedro","family":"Costa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0881-5891","authenticated-orcid":false,"given":"Min","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,8,3]]},"reference":[{"issue":"3","key":"29_CR1","doi-asserted-by":"publisher","first-page":"231","DOI":"10.5566\/ias.1155","volume":"33","author":"E Decenci\u00e9re","year":"2004","unstructured":"Decenci\u00e9re, E., et al.: Feedback on a publicly distributed image database: the messidor database. Image Anal. Stereol. 33(3), 231\u2013234 (2004). https:\/\/doi.org\/10.5566\/ias.1155","journal-title":"Image Anal. Stereol."},{"key":"29_CR2","doi-asserted-by":"crossref","unstructured":"Niemeijer, M., Staal, J.J., van Ginneken, B., Loog, M., Abramoff, M.D.: Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Michael Fitzpatrick, J., Sonka, M. (eds.) SPIE Medical Imaging. SPIE, vol. 5370, pp. 648\u2013656 (2004)","DOI":"10.1117\/12.535349"},{"issue":"8","key":"29_CR3","doi-asserted-by":"publisher","first-page":"951","DOI":"10.1109\/TMI.2003.815900","volume":"22","author":"A Hoover","year":"2003","unstructured":"Hoover, A., Goldbaum, M.: Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans. Med. Imaging 22(8), 951\u2013958 (2003)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"29_CR4","doi-asserted-by":"crossref","unstructured":"Kauppi, T., et al.: DIARETDB1 diabetic retinopathy database and evaluation protocol. In: Proceedings of the 11th Conference on Medical Image Understanding and Analysis (2007)","DOI":"10.5244\/C.21.15"},{"key":"29_CR5","doi-asserted-by":"publisher","unstructured":"Indian Diabetic Retinopathy Image Dataset. https:\/\/doi.org\/10.21227\/H25W98 . Accessed 14 Mar 2019","DOI":"10.21227\/H25W98"},{"key":"29_CR6","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.media.2017.04.012","volume":"39","author":"G Quellec","year":"2017","unstructured":"Quellec, G., Charri\u00e9re, K., Boudi, Y., Cochener, B., Lamard, M.: Deep image mining for diabetic retinopathy screening. Med. Image Anal. 39, 178\u2013193 (2017)","journal-title":"Med. Image Anal."},{"key":"29_CR7","doi-asserted-by":"crossref","unstructured":"Xie, S., Tu, Z.: Holistically-nested edge detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1395\u20131403 (2015)","DOI":"10.1109\/ICCV.2015.164"},{"key":"29_CR8","first-page":"234","volume-title":"Lecture Notes in Computer Science","author":"Olaf Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. arXiv:1505.04597 (2015)"},{"key":"29_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"},{"key":"29_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/978-3-030-11726-9_17","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"D Lachinov","year":"2019","unstructured":"Lachinov, D., Vasiliev, E., Turlapov, V.: Glioma segmentation with cascaded UNet. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 189\u2013198. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11726-9_17"},{"key":"29_CR11","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","volume":"37","author":"X Li","year":"2018","unstructured":"Li, X., Chen, H., Qi, X., Dou, Q., Fu, C., Heng, P.: H-DenseUNet: hybrid densely connected unet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imaging 37, 2663\u20132674 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"22","key":"29_CR12","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama 316(22), 2402\u20132410 (2016)","journal-title":"Jama"},{"key":"29_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1007\/978-3-319-46723-8_27","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"C Payer","year":"2016","unstructured":"Payer, C., \u0160tern, D., Bischof, H., Urschler, M.: Regressing heatmaps for multiple landmark localization using CNNs. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 230\u2013238. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_27"},{"key":"29_CR14","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":"29_CR15","unstructured":"Luc, P., Couprie, C., Chintala, S., Verbeek, J.: Semantic segmentation using adversarial networks. arXiv:1611.08408 (2016)"},{"key":"29_CR16","unstructured":"Hung, W.C., Tsai, Y.H., Liou, Y.T., Lin, Y.Y., Yang, M.H.: Adversarial learning for semi-supervised semantic segmentation. arXiv preprint arXiv:1802.07934 (2018)"},{"issue":"1","key":"29_CR17","doi-asserted-by":"publisher","first-page":"5110","DOI":"10.1038\/s41598-017-05300-5","volume":"7","author":"M Ghafoorian","year":"2017","unstructured":"Ghafoorian, M., et al.: Location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities. Sci. Rep. 7(1), 5110 (2017)","journal-title":"Sci. Rep."},{"issue":"5","key":"29_CR18","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1109\/TMI.2016.2536809","volume":"35","author":"A Setio","year":"2016","unstructured":"Setio, A., et al.: Pulmonary nodule detection in ct images: false positive reduction using multi-view convolutional networks. IEEE Trans. Med. Imaging 35(5), 1160\u20131169 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"29_CR19","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"29_CR20","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":"29_CR21","doi-asserted-by":"crossref","unstructured":"Costa, P., et al.: Eyewes: weakly supervised pre-trained convolutional neural networks for diabetic retinopathy detection (2018)","DOI":"10.23919\/MVA.2019.8757991"},{"key":"29_CR22","doi-asserted-by":"crossref","unstructured":"Huo, Y., et al.: Splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks. In: Medical Imaging 2018: Image Processing, vol. 10574. International Society for Optics and Photonics (2018)","DOI":"10.1117\/12.2293406"},{"key":"29_CR23","unstructured":"Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)"},{"issue":"1","key":"29_CR24","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1023\/B:VLSI.0000028532.53893.82","volume":"38","author":"A Reza","year":"2004","unstructured":"Reza, A.: Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. J. VLSI Signal Process. Syst. Signal Image Video Technol. 38(1), 35\u201344 (2004)","journal-title":"J. VLSI Signal Process. Syst. Signal Image Video Technol."},{"key":"29_CR25","doi-asserted-by":"crossref","first-page":"208","DOI":"10.5201\/ipol.2011.bcm_nlm","volume":"1","author":"A Buades","year":"2011","unstructured":"Buades, A., Coll, B., Morel, J.: Non-local means denoising. Image Process. Line 1, 208\u2013212 (2011)","journal-title":"Image Process. Line"},{"key":"29_CR26","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2172\u20132180 (2016)"},{"key":"29_CR27","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J., Zhou, T., Efros, A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Recognition"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-27272-2_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T12:47:20Z","timestamp":1664110040000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-27272-2_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030272715","9783030272722"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-27272-2_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"3 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Waterloo, ON","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","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":"27 August 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciar2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.aimiconf.org\/iciar19\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"ConfTool","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"142","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":"58","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":"26","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":"41% - 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":"2.7","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":"3.8","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)"}}]}}