{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T11:04:50Z","timestamp":1771326290973,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031336577","type":"print"},{"value":"9783031336584","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\/978-3-031-33658-4_14","type":"book-chapter","created":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T08:02:15Z","timestamp":1685347335000},"page":"146-160","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Segmentation, Classification, and\u00a0Quality Assessment of\u00a0UW-OCTA Images for\u00a0the\u00a0Diagnosis of\u00a0Diabetic Retinopathy"],"prefix":"10.1007","author":[{"given":"Yihao","family":"Li","sequence":"first","affiliation":[]},{"given":"Rachid","family":"Zeghlache","sequence":"additional","affiliation":[]},{"given":"Ikram","family":"Brahim","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Yubo","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Pierre-Henri","family":"Conze","sequence":"additional","affiliation":[]},{"given":"Mathieu","family":"Lamard","sequence":"additional","affiliation":[]},{"given":"Gwenol\u00e9","family":"Quellec","sequence":"additional","affiliation":[]},{"given":"Mostafa","family":"El Habib Daho","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,30]]},"reference":[{"key":"14_CR1","doi-asserted-by":"publisher","first-page":"28642","DOI":"10.1109\/ACCESS.2022.3157632","volume":"10","author":"MZ Atwany","year":"2022","unstructured":"Atwany, M.Z., Sahyoun, A.H., Yaqub, M.: Deep learning techniques for diabetic retinopathy classification: a survey. IEEE Access 10, 28642\u201328655 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3157632","journal-title":"IEEE Access"},{"key":"14_CR2","doi-asserted-by":"publisher","unstructured":"Dai, L., et al.:A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nature Commun. 12(1) (Dec 2021). https:\/\/doi.org\/10.1038\/s41467-021-23458-5","DOI":"10.1038\/s41467-021-23458-5"},{"issue":"1","key":"14_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s40942-015-0005-8","volume":"1","author":"TE De Carlo","year":"2015","unstructured":"De Carlo, T.E., Romano, A., Waheed, N.K., Duker, J.S.: A review of optical coherence tomography angiography (octa). Int. J. Retina Vitreous 1(1), 5 (2015). https:\/\/doi.org\/10.1186\/s40942-015-0005-8","journal-title":"Int. J. Retina Vitreous"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015). 10.48550\/ARXIV.1512.03385","DOI":"10.1109\/CVPR.2016.90"},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks (2016). 10.48550\/ARXIV.1608.06993, https:\/\/arxiv.org\/abs\/1608.06993","DOI":"10.1109\/CVPR.2017.243"},{"key":"14_CR6","unstructured":"Isensee, F., et al.: nnu-net: Self-adapting framework for u-net-based medical image segmentation (2018). 10.48550\/ARXIV.1809.10486, https:\/\/arxiv.org\/abs\/1809.10486"},{"key":"14_CR7","unstructured":"Lee, D.H., et al.: Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on Challenges in Representation Learning, ICML. vol. 3, p. 896 (2013)"},{"key":"14_CR8","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/978-3-031-16525-2_6","volume-title":"Ophthalmic Medical Image Analysis","author":"Y Li","year":"2022","unstructured":"Li, Y., et al.: Multimodal information fusion for glaucoma and diabetic retinopathy classification. In: Antony, B., Fu, H., Lee, C.S., MacGillivray, T., Xu, Y., Zheng, Y. (eds.) Ophthalmic Medical Image Analysis, pp. 53\u201362. Springer International Publishing, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16525-2_6"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Liu, R., et al.: Deepdrid: Diabetic retinopathy-grading and image quality estimation challenge. Patterns 3(6), 100512 (2022)","DOI":"10.1016\/j.patter.2022.100512"},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al: Swin transformer: Hierarchical vision transformer using shifted windows (2021). 10.48550\/ARXIV.2103.14030, https:\/\/arxiv.org\/abs\/2103.14030","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s (2022). 10.48550\/ARXIV.2201.03545, https:\/\/arxiv.org\/abs\/2201.03545","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation (2016)","DOI":"10.1109\/3DV.2016.79"},{"issue":"7","key":"14_CR13","doi-asserted-by":"publisher","first-page":"F0259","DOI":"10.1109\/ACCESS.2022.3157632","volume":"63","author":"G Quellec","year":"2022","unstructured":"Quellec, G., et al.: 3-d style transfer between structure and flow channels in oct angiography. Invest. Ophthalmol. Vis. Sci. 63(7), F0259-2989 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3157632","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"Quellec, G., Al Hajj, H., Lamard, M., Conze, P.H., Massin, P., Cochener, B.: Explain: Explanatory artificial intelligence for diabetic retinopathy diagnosis. Med. Image Anal. 72, 102118 (2021) 10.1016\/j.media.2021.102118, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S136184152100164X","DOI":"10.1016\/j.media.2021.102118"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Quellec, G., Charri\u00e8re, K., Boudi, Y., Cochener, B., Lamard, M.: Deep image mining for diabetic retinopathy screening. Med. Image Anal. 39, 178\u2013193 (2017) 10.1016\/j.media.2017.04.012, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S136184151730066X","DOI":"10.1016\/j.media.2017.04.012"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"14_CR17","doi-asserted-by":"publisher","unstructured":"Russell, J., Shi, Y., Hinkle, J., Scott, N., Fan, K., Lyu, C., Gregori, G., Rosenfeld, P.: Longitudinal wide-field swept-source oct angiography of neovascularization in proliferative diabetic retinopathy after panretinal photocoagulation. ophthalmol retina. Retina 3(4), 350\u2013361 (2019). https:\/\/doi.org\/10.1016\/j.oret.2018.11.008","DOI":"10.1016\/j.oret.2018.11.008"},{"issue":"1","key":"14_CR18","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1097\/IAE.0000000000001938","volume":"39","author":"KB Schaal","year":"2019","unstructured":"Schaal, K.B., Munk, M.R., Wyssmueller, I., Berger, L.E., Zinkernagel, M.S., Wolf, S.: Vascular abnormalities in diabetic retinopathy assessed with swept-source optical coherence tomography angiography widefield imaging. Retina 39(1), 79\u201387 (2019). https:\/\/doi.org\/10.1097\/IAE.0000000000001938","journal-title":"Retina"},{"key":"14_CR19","doi-asserted-by":"publisher","unstructured":"Sheng, B., Chen, X., Li, T., Ma, T., Yang, Y., Bi, L., Zhang, X.: An overview of artificial intelligence in diabetic retinopathy and other ocular diseases. Frontiers in Public Health 10 (2022). https:\/\/doi.org\/10.3389\/fpubh.2022.971943,https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2022.971943","DOI":"10.3389\/fpubh.2022.971943,"},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 761\u2013769 (2016)","DOI":"10.1109\/CVPR.2016.89"},{"key":"14_CR21","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). 10.48550\/ARXIV.1409.1556, https:\/\/arxiv.org\/abs\/1409.1556"},{"key":"14_CR22","unstructured":"Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks (2019). 10.48550\/ARXIV.1905.11946, https:\/\/arxiv.org\/abs\/1905.11946"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Tian, M., Wolf, S., Munk, M.R., Schaal, K.B.: Evaluation of different swept\u2019source optical coherence tomography angiography (ss-octa) slabs for the detection of features of diabetic retinopathy. Acta Ophthalmologica 98(4), e416\u2013e420 (2020) 10.1111\/aos.14299, https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1111\/aos.14299","DOI":"10.1111\/aos.14299"},{"key":"14_CR24","doi-asserted-by":"publisher","unstructured":"Wightman, R.: Pytorch image models. https:\/\/github.com\/rwightman\/pytorch-image-models (2019). https:\/\/doi.org\/10.5281\/zenodo.4414861","DOI":"10.5281\/zenodo.4414861"},{"key":"14_CR25","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/978-3-031-16525-2_5","volume-title":"Ophthalmic Medical Image Analysis","author":"R Zeghlache","year":"2022","unstructured":"Zeghlache, R., et al.: Detection of diabetic retinopathy using longitudinal self supervised learning. In: Antony, B., Fu, H., Lee, C.S., MacGillivray, T., Xu, Y., Zheng, Y. (eds.) Ophthalmic Medical Image Analysis, pp. 43\u201352. Springer International Publishing, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16525-2_5"},{"key":"14_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Rezaei, K.A., Saraf, S.S., Chu, Z., Wang, F., Wang, R.K.: Ultra-wide optical coherence tomography angiography in diabetic retinopathy. Quant. Imaging Med. Surgery 8(8) (2018), https:\/\/qims.amegroups.com\/article\/view\/21249","DOI":"10.21037\/qims.2018.09.02"}],"container-title":["Lecture Notes in Computer Science","Mitosis Domain Generalization and Diabetic Retinopathy Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-33658-4_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T08:03:54Z","timestamp":1685347434000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-33658-4_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031336577","9783031336584"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-33658-4_14","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":"30 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DRAC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Challenge on Diabetic Retinopathy Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"drac2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/drac22.grand-challenge.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}