{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T11:09:14Z","timestamp":1771067354888,"version":"3.50.1"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031439896","type":"print"},{"value":"9783031439902","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\/978-3-031-43990-2_61","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:07:48Z","timestamp":1696115268000},"page":"649-659","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["VF-HM: Vision Loss Estimation Using Fundus Photograph for\u00a0High Myopia"],"prefix":"10.1007","author":[{"given":"Zipei","family":"Yan","sequence":"first","affiliation":[]},{"given":"Dong","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Linchuan","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Jiahang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zhengji","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jiannong","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Chea-su","family":"Kee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"61_CR1","doi-asserted-by":"crossref","unstructured":"Bar-David, D., Bar-David, L., Soudry, S., Fischer, A.: Impact of data augmentation on retinal oct image segmentation for diabetic macular edema analysis. In: MICCAI, pp. 148\u2013158 (2021)","DOI":"10.1007\/978-3-030-87000-3_16"},{"key":"61_CR2","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.patrec.2020.11.008","volume":"140","author":"W Cao","year":"2020","unstructured":"Cao, W., Mirjalili, V., Raschka, S.: Rank consistent ordinal regression for neural networks with application to age estimation. Pattern Recogn. Lett. 140, 325\u2013331 (2020)","journal-title":"Pattern Recogn. Lett."},{"issue":"3","key":"61_CR3","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.ophtha.2019.09.036","volume":"127","author":"M Christopher","year":"2020","unstructured":"Christopher, M., et al.: Deep learning approaches predict glaucomatous visual field damage from OCT optic nerve head EN face images and retinal nerve fiber layer thickness maps. Ophthalmology 127(3), 346\u2013356 (2020)","journal-title":"Ophthalmology"},{"issue":"1","key":"61_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-91493-9","volume":"11","author":"S Datta","year":"2021","unstructured":"Datta, S., Mariottoni, E.B., Dov, D., Jammal, A.A., Carin, L., Medeiros, F.A.: Retinervenet: using recursive deep learning to estimate pointwise 24\u20132 visual field data based on retinal structure. Sci. Rep. 11(1), 1\u201310 (2021)","journal-title":"Sci. Rep."},{"key":"61_CR5","unstructured":"Dery, L.M., Dauphin, Y.N., Grangier, D.: Auxiliary task update decomposition: The good, the bad and the neutral. In: ICLR (2021)"},{"key":"61_CR6","unstructured":"Du, Y., Czarnecki, W.M., Jayakumar, S.M., Farajtabar, M., Pascanu, R., Lakshminarayanan, B.: Adapting auxiliary losses using gradient similarity. arXiv preprint arXiv:1812.02224 (2018)"},{"issue":"8","key":"61_CR7","doi-asserted-by":"publisher","first-page":"1595","DOI":"10.1016\/j.ophtha.2009.11.003","volume":"117","author":"K Hayashi","year":"2010","unstructured":"Hayashi, K., et al.: Long-term pattern of progression of myopic maculopathy: a natural history study. Ophthalmology 117(8), 1595\u20131611 (2010)","journal-title":"Ophthalmology"},{"key":"61_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"61_CR9","doi-asserted-by":"crossref","unstructured":"He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., Li, M.: Bag of tricks for image classification with convolutional neural networks. In: CVPR, pp. 558\u2013567 (2019)","DOI":"10.1109\/CVPR.2019.00065"},{"issue":"5","key":"61_CR10","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.1016\/j.ophtha.2016.01.006","volume":"123","author":"BA Holden","year":"2016","unstructured":"Holden, B.A., et al.: Global prevalence of myopia and high myopia and temporal trends from 2000 through 2050. Ophthalmology 123(5), 1036\u20131042 (2016)","journal-title":"Ophthalmology"},{"issue":"1","key":"61_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-78144-1","volume":"10","author":"J Lee","year":"2020","unstructured":"Lee, J., et al.: Estimating visual field loss from monoscopic optic disc photography using deep learning model. Sci. Rep. 10(1), 1\u201310 (2020)","journal-title":"Sci. Rep."},{"issue":"7","key":"61_CR12","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1016\/S0161-6420(86)33647-9","volume":"93","author":"RA Lewis","year":"1986","unstructured":"Lewis, R.A., Johnson, C.A., Keltner, J.L., Labermeier, P.K.: Variability of quantitative automated perimetry in normal observers. Ophthalmology 93(7), 878\u2013881 (1986)","journal-title":"Ophthalmology"},{"key":"61_CR13","doi-asserted-by":"crossref","unstructured":"Li, L., Lin, H.: Ordinal regression by extended binary classification. In: NeurIPS, pp. 865\u2013872 (2006)","DOI":"10.7551\/mitpress\/7503.003.0113"},{"issue":"7","key":"61_CR14","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1016\/j.ophtha.2022.03.001","volume":"129","author":"F Lin","year":"2022","unstructured":"Lin, F., et al.: Classification of visual field abnormalities in highly myopic eyes without pathologic change. Ophthalmology 129(7), 803\u2013812 (2022)","journal-title":"Ophthalmology"},{"key":"61_CR15","doi-asserted-by":"crossref","unstructured":"M\u00fcller, S.G., Hutter, F.: Trivialaugment: tuning-free yet state-of-the-art data augmentation. In: ICCV, pp. 754\u2013762 (2021)","DOI":"10.1109\/ICCV48922.2021.00081"},{"issue":"5","key":"61_CR16","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1167\/iovs.62.5.5","volume":"62","author":"K Ohno-Matsui","year":"2021","unstructured":"Ohno-Matsui, K., et al.: IMI pathologic myopia. Investigative Ophthalmol. Visual Sci. 62(5), 5\u20135 (2021)","journal-title":"Investigative Ophthalmol. Visual Sci."},{"issue":"3","key":"61_CR17","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1089\/tmj.2015.0068","volume":"22","author":"N Panwar","year":"2016","unstructured":"Panwar, N., et al.: Fundus photography in the 21st century-a review of recent technological advances and their implications for worldwide healthcare. Telemedicine and e-Health 22(3), 198\u2013208 (2016)","journal-title":"Telemedicine and e-Health"},{"issue":"7","key":"61_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0234902","volume":"15","author":"K Park","year":"2020","unstructured":"Park, K., Kim, J., Lee, J.: A deep learning approach to predict visual field using optical coherence tomography. PLoS ONE 15(7), 1\u201319 (2020)","journal-title":"PLoS ONE"},{"issue":"4","key":"61_CR19","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1111\/cxo.12551","volume":"100","author":"J Phu","year":"2017","unstructured":"Phu, J., Khuu, S.K., Yapp, M., Assaad, N., Hennessy, M.P., Kalloniatis, M.: The value of visual field testing in the era of advanced imaging: clinical and psychophysical perspectives. Clin. Exp. Optom. 100(4), 313\u2013332 (2017)","journal-title":"Clin. Exp. Optom."},{"issue":"8","key":"61_CR20","doi-asserted-by":"publisher","first-page":"3627","DOI":"10.1364\/BOE.8.003627","volume":"8","author":"AG Roy","year":"2017","unstructured":"Roy, A.G., et al.: Relaynet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed. Opt. Express 8(8), 3627\u20133642 (2017)","journal-title":"Biomed. Opt. Express"},{"issue":"4","key":"61_CR21","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1159\/000339893","volume":"228","author":"R Silva","year":"2012","unstructured":"Silva, R.: Myopic maculopathy: a review. Ophthalmologica 228(4), 197\u2013213 (2012)","journal-title":"Ophthalmologica"},{"key":"61_CR22","unstructured":"Vivien: Learning through auxiliary tasks. https:\/\/vivien000.github.io\/blog\/journal\/learning-though-auxiliary_tasks.html"},{"key":"61_CR23","doi-asserted-by":"crossref","unstructured":"Wang, S., Yan, Z., Zhang, D., Wei, H., Li, Z., Li, R.: Prototype knowledge distillation for medical segmentation with missing modality. In: ICASSP (2023)","DOI":"10.1109\/ICASSP49357.2023.10095014"},{"key":"61_CR24","doi-asserted-by":"crossref","unstructured":"Wang, S., Zhang, D., Yan, Z., Zhang, J., Li, R.: Feature alignment and uniformity for test time adaptation. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.01920"},{"issue":"1","key":"61_CR25","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.ajo.2013.08.010","volume":"157","author":"TY Wong","year":"2014","unstructured":"Wong, T.Y., Ferreira, A., Hughes, R., Carter, G., Mitchell, P.: Epidemiology and disease burden of pathologic myopia and myopic choroidal neovascularization: an evidence-based systematic review. Am. J. Ophthalmol. 157(1), 9-25.e12 (2014)","journal-title":"Am. J. Ophthalmol."},{"issue":"12","key":"61_CR26","doi-asserted-by":"publisher","first-page":"4880","DOI":"10.1167\/iovs.18-24471","volume":"59","author":"O Xiao","year":"2018","unstructured":"Xiao, O., et al.: Distribution and severity of myopic maculopathy among highly myopic eyes. Investigative Ophthalmol. Visual Sci. 59(12), 4880\u20134885 (2018)","journal-title":"Investigative Ophthalmol. Visual Sci."},{"issue":"4","key":"61_CR27","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1167\/iovs.63.4.13","volume":"63","author":"S Xie","year":"2022","unstructured":"Xie, S., et al.: Structural abnormalities in the papillary and peripapillary areas and corresponding visual field defects in eyes with pathologic myopia. Investigative Ophthal. Visual Sci. 63(4), 13\u201313 (2022)","journal-title":"Investigative Ophthal. Visual Sci."},{"key":"61_CR28","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.ajo.2020.04.037","volume":"218","author":"L Xu","year":"2020","unstructured":"Xu, L., et al.: Predicting the glaucomatous central 10-degree visual field from optical coherence tomography using deep learning and tensor regression. Am. J. Ophthalmol. 218, 304\u2013313 (2020)","journal-title":"Am. J. Ophthalmol."},{"key":"61_CR29","doi-asserted-by":"crossref","unstructured":"Xu, L., Asaoka, R., Kiwaki, T., Murata, H., Fujino, Y., Yamanishi, K.: Pami: a computational module for joint estimation and progression prediction of glaucoma. In: KDD, pp. 3826\u20133834 (2021)","DOI":"10.1145\/3447548.3467195"},{"issue":"1","key":"61_CR30","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.ogla.2020.08.002","volume":"4","author":"L Xu","year":"2021","unstructured":"Xu, L., et al.: Improving visual field trend analysis with oct and deeply regularized latent-space linear regression. Ophthalmol. Glaucoma 4(1), 78\u201388 (2021)","journal-title":"Ophthalmol. Glaucoma"},{"key":"61_CR31","unstructured":"Zhang, S., Yang, L., Mi, M.B., Zheng, X., Yao, A.: Improving deep regression with ordinal entropy. In: ICLR (2023)"},{"issue":"3","key":"61_CR32","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1097\/IAE.0000000000002418","volume":"40","author":"X Zhao","year":"2020","unstructured":"Zhao, X., et al.: Morphological characteristics and visual acuity of highly myopic eyes with different severities of myopic maculopathy. Retina 40(3), 461\u2013467 (2020)","journal-title":"Retina"},{"key":"61_CR33","doi-asserted-by":"crossref","unstructured":"Zheng, Y., et al.: Glaucoma progression prediction using retinal thickness via latent space linear regression. In: KDD, pp. 2278\u20132286 (2019)","DOI":"10.1145\/3292500.3330757"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43990-2_61","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T15:42:47Z","timestamp":1710171767000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43990-2_61"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439896","9783031439902"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43990-2_61","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":"1 October 2023","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":"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":"8 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":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","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":"730","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":"32% - 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":"5","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)"}}]}}