{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T16:54:14Z","timestamp":1776272054229,"version":"3.50.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031440120","type":"print"},{"value":"9783031440137","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\/978-3-031-44013-7_2","type":"book-chapter","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T23:02:39Z","timestamp":1694818959000},"page":"11-20","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Improved Automatic Diabetic Retinopathy Severity Classification Using Deep Multimodal Fusion of\u00a0UWF-CFP and\u00a0OCTA Images"],"prefix":"10.1007","author":[{"given":"Mostafa","family":"El Habib Daho","sequence":"first","affiliation":[]},{"given":"Yihao","family":"Li","sequence":"additional","affiliation":[]},{"given":"Rachid","family":"Zeghlache","sequence":"additional","affiliation":[]},{"given":"Yapo Cedric","family":"Atse","sequence":"additional","affiliation":[]},{"given":"Hugo","family":"Le Boit\u00e9","sequence":"additional","affiliation":[]},{"given":"Sophie","family":"Bonnin","sequence":"additional","affiliation":[]},{"given":"Deborah","family":"Cosette","sequence":"additional","affiliation":[]},{"given":"Pierre","family":"Deman","sequence":"additional","affiliation":[]},{"given":"Laurent","family":"Borderie","sequence":"additional","affiliation":[]},{"given":"Capucine","family":"Lepicard","sequence":"additional","affiliation":[]},{"given":"Ramin","family":"Tadayoni","sequence":"additional","affiliation":[]},{"given":"B\u00e9atrice","family":"Cochener","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":[]}],"member":"297","published-online":{"date-parts":[[2023,9,16]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","unstructured":"Early treatment diabetic retinopathy study design and baseline patient characteristics: Etdrs report number 7. Ophthalmology 98(5, Supplement), 741\u2013756 (1991). https:\/\/doi.org\/10.1016\/S0161-6420(13)38009-9","DOI":"10.1016\/S0161-6420(13)38009-9"},{"key":"2_CR2","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1007\/s10278-018-0093-8","volume":"31","author":"M Akhavan Aghdam","year":"2018","unstructured":"Akhavan Aghdam, M., Sharifi, A., Pedram, M.M.: Combination of RS-fMRI and SMRI data to discriminate autism spectrum disorders in young children using deep belief network. J. Dig. Imaging 31, 895\u2013903 (2018)","journal-title":"J. Dig. Imaging"},{"issue":"12","key":"2_CR3","doi-asserted-by":"publisher","first-page":"4310","DOI":"10.3390\/s22124310","volume":"22","author":"HR Al-Absi","year":"2022","unstructured":"Al-Absi, H.R., Islam, M.T., Refaee, M.A., Chowdhury, M.E., Alam, T.: Cardiovascular disease diagnosis from DXA scan and retinal images using deep learning. Sensors 22(12), 4310 (2022)","journal-title":"Sensors"},{"key":"2_CR4","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.neucom.2020.05.087","volume":"412","author":"S El-Sappagh","year":"2020","unstructured":"El-Sappagh, S., Abuhmed, T., Islam, S.R., Kwak, K.S.: Multimodal multitask deep learning model for Alzheimer\u2019s disease progression detection based on time series data. Neurocomputing 412, 197\u2013215 (2020)","journal-title":"Neurocomputing"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Hao, X., et al.: Mixgen: a new multi-modal data augmentation (2023)","DOI":"10.1109\/WACVW58289.2023.00042"},{"key":"2_CR6","doi-asserted-by":"publisher","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 7132\u20137141 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00745","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2_CR7","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.patrec.2021.08.035","volume":"152","author":"I Lahsaini","year":"2021","unstructured":"Lahsaini, I., El Habib Daho, M., Chikh, M.A.: Deep transfer learning based classification model for COVID-19 using chest CT-scans. Pattern Recogn. Lett. 152, 122\u2013128 (2021). https:\/\/doi.org\/10.1016\/j.patrec.2021.08.035","journal-title":"Pattern Recogn. Lett."},{"key":"2_CR8","doi-asserted-by":"publisher","unstructured":"Li, J., et al.: Ultra-widefield color fundus photography combined with high-speed ultra-widefield swept-source optical coherence tomography angiography for non-invasive detection of lesions in diabetic retinopathy. Front. Public Health 10 (2022). https:\/\/doi.org\/10.3389\/fpubh.2022.1047608","DOI":"10.3389\/fpubh.2022.1047608"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Li, T., et al.: Applications of deep learning in fundus images: a review (2021). https:\/\/arxiv.org\/abs\/2101.09864","DOI":"10.1016\/j.media.2021.101971"},{"key":"2_CR10","doi-asserted-by":"publisher","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.) OMIA 2022. LNCS, vol. 13576, pp. 53\u201362. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16525-2_6","DOI":"10.1007\/978-3-031-16525-2_6"},{"key":"2_CR11","doi-asserted-by":"publisher","unstructured":"Lin, R., Hu, H.: Adapt and explore: multimodal mixup for representation learning. Available at SSRN (2023). https:\/\/doi.org\/10.2139\/ssrn.4461697","DOI":"10.2139\/ssrn.4461697"},{"key":"2_CR12","unstructured":"Liu, Z., et al.: Learning multimodal data augmentation in feature space (2023)"},{"key":"2_CR13","doi-asserted-by":"publisher","first-page":"3023","DOI":"10.1007\/s00330-019-06610-0","volume":"30","author":"X Qian","year":"2020","unstructured":"Qian, X., et al.: A combined ultrasonic b-mode and color doppler system for the classification of breast masses using neural network. Eur. Radiol. 30, 3023\u20133033 (2020)","journal-title":"Eur. Radiol."},{"key":"2_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102118","volume":"72","author":"G Quellec","year":"2021","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). https:\/\/doi.org\/10.1016\/j.media.2021.102118","journal-title":"Med. Image Anal."},{"key":"2_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102802","volume":"88","author":"F Shamshad","year":"2023","unstructured":"Shamshad, F., et al.: Transformers in medical imaging: a survey. Med. Image Anal. 88, 102802 (2023). https:\/\/doi.org\/10.1016\/j.media.2023.102802","journal-title":"Med. Image Anal."},{"issue":"12","key":"2_CR16","doi-asserted-by":"publisher","first-page":"2465","DOI":"10.1016\/j.ophtha.2015.07.034","volume":"122","author":"PS Silva","year":"2015","unstructured":"Silva, P.S., et al.: Diabetic retinopathy severity and peripheral lesions are associated with nonperfusion on ultrawide field angiography. Ophthalmology 122(12), 2465\u20132472 (2015). https:\/\/doi.org\/10.1016\/j.ophtha.2015.07.034","journal-title":"Ophthalmology"},{"key":"2_CR17","doi-asserted-by":"publisher","unstructured":"Sleeman, W.C., Kapoor, R., Ghosh, P.: Multimodal classification: current landscape, taxonomy and future directions. ACM Comput. Surv. 55(7) (2022). https:\/\/doi.org\/10.1145\/3543848","DOI":"10.1145\/3543848"},{"issue":"11","key":"2_CR18","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1038\/s41433-020-01233-y","volume":"35","author":"Z Sun","year":"2021","unstructured":"Sun, Z., Yang, D., Tang, Z., et al.: Optical coherence tomography angiography in diabetic retinopathy: an updated review. Eye 35(11), 149\u2013161 (2021). https:\/\/doi.org\/10.1038\/s41433-020-01233-y","journal-title":"Eye"},{"issue":"11","key":"2_CR19","doi-asserted-by":"publisher","first-page":"1580","DOI":"10.1016\/j.ophtha.2021.04.027","volume":"128","author":"ZL Teo","year":"2021","unstructured":"Teo, Z.L., et al.: Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis. Ophthalmology 128(11), 1580\u20131591 (2021)","journal-title":"Ophthalmology"},{"key":"2_CR20","unstructured":"Verma, V., et al.: Manifold mixup: better representations by interpolating hidden states (2019)"},{"issue":"3","key":"2_CR21","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1136\/bjophthalmol-2020-317659","volume":"106","author":"CE Wisely","year":"2022","unstructured":"Wisely, C.E., et al.: Convolutional neural network to identify symptomatic Alzheimer\u2019s disease using multimodal retinal imaging. Br. J. Ophthalmol. 106(3), 388\u2013395 (2022). https:\/\/doi.org\/10.1136\/bjophthalmol-2020-317659","journal-title":"Br. J. Ophthalmol."},{"key":"2_CR22","unstructured":"Wu, J., et al.: Gamma challenge: glaucoma grading from multi-modality images. arXiv preprint arXiv:2202.06511 (2022)"},{"issue":"2","key":"2_CR23","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.ophtha.2021.07.032","volume":"129","author":"J Xiong","year":"2022","unstructured":"Xiong, J., et al.: Multimodal machine learning using visual fields and peripapillary circular oct scans in detection of glaucomatous optic neuropathy. Ophthalmology 129(2), 171\u2013180 (2022)","journal-title":"Ophthalmology"},{"issue":"1","key":"2_CR24","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1186\/s12886-021-01933-3","volume":"21","author":"J Yang","year":"2021","unstructured":"Yang, J., Zhang, B., Wang, E., et al.: Ultra-wide field swept-source optical coherence tomography angiography in patients with diabetes without clinically detectable retinopathy. BMC Ophthalmol. 21(1), 192 (2021). https:\/\/doi.org\/10.1186\/s12886-021-01933-3","journal-title":"BMC Ophthalmol."},{"issue":"7","key":"2_CR25","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1167\/tvst.11.7.10","volume":"11","author":"P Zang","year":"2022","unstructured":"Zang, P., et al.: A diabetic retinopathy classification framework based on deep-learning analysis of oct angiography. Transl. Vision Sci. Technol. 11(7), 10\u201310 (2022)","journal-title":"Transl. Vision Sci. Technol."},{"key":"2_CR26","unstructured":"Zhang, H., Ciss\u00e9, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. CoRR abs\/1710.09412 (2017). https:\/\/arxiv.org\/abs\/1710.09412"},{"key":"2_CR27","doi-asserted-by":"publisher","unstructured":"Zhao, X., Chen, Y., Liu, S., Zang, X., Xiang, Y., Tang, B.: TMMDA: a new token mixup multimodal data augmentation for multimodal sentiment analysis. In: Proceedings of the ACM Web Conference 2023. WWW 2023, pp. 1714\u20131722. Association for Computing Machinery (2023). https:\/\/doi.org\/10.1145\/3543507.3583406","DOI":"10.1145\/3543507.3583406"},{"issue":"9","key":"2_CR28","doi-asserted-by":"publisher","first-page":"4077","DOI":"10.1002\/mp.14255","volume":"47","author":"W Zong","year":"2020","unstructured":"Zong, W., Lee, J.K., Liu, C., Carver, E.N., Feldman, A.M., Janic, E.A.: A deep dive into understanding tumor foci classification using multiparametric MRI based on convolutional neural network. Med. Phys. 47(9), 4077\u20134086 (2020)","journal-title":"Med. Phys."}],"container-title":["Lecture Notes in Computer Science","Ophthalmic Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44013-7_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T23:11:40Z","timestamp":1695078700000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44013-7_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031440120","9783031440137"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44013-7_2","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":"16 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"OMIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Ophthalmic Medical Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"omia2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/omiax\/","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":"CMT System","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27","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":"16","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":"59% - 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":"3","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)"}}]}}