{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T16:47:47Z","timestamp":1776876467941,"version":"3.51.2"},"reference-count":58,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T00:00:00Z","timestamp":1775433600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Korea Ministry of Science and ICT","doi-asserted-by":"publisher","award":["RS-2024-00336962"],"award-info":[{"award-number":["RS-2024-00336962"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Korea Ministry of Science and ICT","doi-asserted-by":"publisher","award":["IITP-2025-RS-2023-00254177"],"award-info":[{"award-number":["IITP-2025-RS-2023-00254177"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013129","name":"Ministry of SMEs and Startups","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013128","name":"Korea Technology and Information Promotion Agency for Small and Medium Enterprises","doi-asserted-by":"publisher","award":["RS-2023-00273281"],"award-info":[{"award-number":["RS-2023-00273281"]}],"id":[{"id":"10.13039\/501100013128","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.asoc.2026.115158","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T06:54:07Z","timestamp":1774940047000},"page":"115158","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["GlaucoFormer: A transformer-based model for glaucoma detection and segmentation"],"prefix":"10.1016","volume":"197","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6586-5780","authenticated-orcid":false,"given":"Dae-Il","family":"Noh","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1952-7067","authenticated-orcid":false,"given":"Thien-Thanh","family":"Dao","sequence":"additional","affiliation":[]},{"given":"Kyeong-Min","family":"Park","sequence":"additional","affiliation":[]},{"given":"Seon-Geun","family":"Jeong","sequence":"additional","affiliation":[]},{"given":"Won-Joo","family":"Hwang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"11","key":"10.1016\/j.asoc.2026.115158_bib0005","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.1016\/j.ophtha.2014.05.013","article-title":"Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis","volume":"121","author":"Tham","year":"2014","journal-title":"Ophthalmology"},{"issue":"18","key":"10.1016\/j.asoc.2026.115158_bib0010","doi-asserted-by":"crossref","first-page":"1901","DOI":"10.1001\/jama.2014.3192","article-title":"The pathophysiology and treatment of glaucoma: a review","volume":"311","author":"Weinreb","year":"2014","journal-title":"Jama"},{"issue":"4","key":"10.1016\/j.asoc.2026.115158_bib0015","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.1049\/cit2.12154","article-title":"Frequency-to-spectrum mapping GAN for semisupervised hyperspectral anomaly detection","volume":"8","author":"Wang","year":"2023","journal-title":"CAAI Trans. Intell. Technol."},{"key":"10.1016\/j.asoc.2026.115158_bib0020","first-page":"1","article-title":"Global feature-injected blind-spot network for hyperspectral anomaly detection","volume":"21","author":"Wang","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"3","key":"10.1016\/j.asoc.2026.115158_bib0025","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1049\/cit2.12310","article-title":"Multi-granularity feature enhancement network for maritime ship detection","volume":"9","author":"Ying","year":"2024","journal-title":"CAAI Trans. Intell. Technol."},{"issue":"3","key":"10.1016\/j.asoc.2026.115158_bib0030","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1145\/3770082","article-title":"Transformer model embedding dual stream for modulation classification of short signal samples","volume":"17","author":"Dao","year":"2026","journal-title":"ACM Trans. Intell. Syst. Technol."},{"issue":"1","key":"10.1016\/j.asoc.2026.115158_bib0035","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/LCOMM.2023.3336985","article-title":"VT-MCNet: high-accuracy automatic modulation classification model based on vision transformer","volume":"28","author":"Dao","year":"2024","journal-title":"IEEE Commun. Lett."},{"key":"10.1016\/j.asoc.2026.115158_bib0040","doi-asserted-by":"crossref","first-page":"16111","DOI":"10.1109\/ACCESS.2022.3145969","article-title":"FastMDE: a fast CNN architecture for monocular depth estimation at high resolution","volume":"10","author":"Dao","year":"2022","journal-title":"IEEE Access"},{"issue":"10","key":"10.1016\/j.asoc.2026.115158_bib0045","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.3390\/diagnostics13101738","article-title":"Automatic diagnosis of glaucoma from retinal images using deep learning approach","volume":"13","author":"Shoukat","year":"2023","journal-title":"Diagnostics"},{"key":"10.1016\/j.asoc.2026.115158_bib0050","author":"Bajwa"},{"key":"10.1016\/j.asoc.2026.115158_bib0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2022.108009","article-title":"Deep learning-based classification network for glaucoma in retinal images","volume":"101","author":"Juneja","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.asoc.2026.115158_bib0060","first-page":"3065","article-title":"ORIGA-light: an online retinal fundus image database for glaucoma analysis and research","author":"Zhang","year":"2010","journal-title":"Annu. Int. Conf. IEEE Eng. Med. Biol."},{"key":"10.1016\/j.asoc.2026.115158_bib0065","series-title":"REFUGE: Retinal Fundus Glaucoma Challenge","author":"Fu","year":"2019"},{"key":"10.1016\/j.asoc.2026.115158_bib0070","series-title":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","first-page":"53","article-title":"Drishti-GS: retinal image dataset for optic nerve head (ONH) segmentation","author":"Sivaswamy","year":"2014"},{"key":"10.1016\/j.asoc.2026.115158_bib0075","series-title":"2016 International Conference on Control, Decision and Information Technologies (CoDIT)","first-page":"083","article-title":"Study of contour detection methods as applied on optic nerve\u2019s images for glaucoma diagnosis","author":"Soltani","year":"2016"},{"key":"10.1016\/j.asoc.2026.115158_bib0080","article-title":"Glaucoma detection in retinal fundus images using U-Net and supervised machine learning algorithms","volume":"5","author":"Shinde","year":"2021","journal-title":"Intell.-Based Med."},{"key":"10.1016\/j.asoc.2026.115158_bib0085","article-title":"Joint segmentation of optic CUP and optic disc using deep convolutional generative adversarial network","volume":"2234","author":"Yu","year":"2022","journal-title":"J. Phys.: Conf. Ser."},{"key":"10.1016\/j.asoc.2026.115158_bib0090","doi-asserted-by":"crossref","first-page":"153985","DOI":"10.1109\/ACCESS.2021.3128174","article-title":"Minimizing-entropy and Fourier consistency network for domain adaptation on optic disc and CUP segmentation","volume":"9","author":"Xu","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2026.115158_bib0095","doi-asserted-by":"crossref","first-page":"102733","DOI":"10.1109\/ACCESS.2020.2998635","article-title":"CDED-net: joint segmentation of optic disc and optic CUP for glaucoma screening","volume":"8","author":"Tabassum","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2026.115158_bib0100","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1007\/s11760-020-01815-z","article-title":"Application of an attention U-Net incorporating transfer learning for optic disc and CUP segmentation","volume":"15","author":"Zhao","year":"2020","journal-title":"Signal Image Video Process."},{"key":"10.1016\/j.asoc.2026.115158_bib0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107209","article-title":"TUNet and domain adaptation based learning for joint optic disc and CUP segmentation","volume":"163","author":"Li","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.asoc.2026.115158_bib0110","series-title":"Domain Generalization via Pareto Optimal Gradient Matching","author":"Nguyen","year":"2025"},{"key":"10.1016\/j.asoc.2026.115158_bib0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110038","article-title":"Optimizing optic CUP and optic disc delineation: introducing the efficient feature preservation segmentation network","volume":"144","author":"Abidin","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.asoc.2026.115158_bib0120","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"20471","article-title":"Harvard Glaucoma detection and progression: a multimodal multitask dataset and generalization-reinforced semi-supervised learning","author":"Luo","year":"2023"},{"key":"10.1016\/j.asoc.2026.115158_bib0125","series-title":"2018 25th IEEE International Conference on Image Processing (ICIP)","first-page":"2227","article-title":"Automatic optic disk and CUP segmentation of fundus images using deep learning","author":"Edupuganti","year":"2018"},{"key":"10.1016\/j.asoc.2026.115158_bib0130","article-title":"An automatic recognition of glaucoma in Fundus images using deep learning and random forest classifier","volume":"109","author":"Shanmugam","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115158_bib0135","doi-asserted-by":"crossref","first-page":"706","DOI":"10.37624\/IJERT\/13.4.2020.706-714","article-title":"Optic disc and optic CUP segmentation methodology for glaucoma detection","volume":"13","author":"Aouf","year":"2020","journal-title":"Int. J. Eng. Res. Technol."},{"key":"10.1016\/j.asoc.2026.115158_bib0140","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-99605-1","article-title":"Deep learning on Fundus images detects glaucoma beyond the optic disc","volume":"11","author":"Hemelings","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.asoc.2026.115158_bib0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106989","article-title":"DEEP GD: deep learning based snapshot ensemble CNN with efficientnet for glaucoma detection","volume":"100","author":"Geetha","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"issue":"1","key":"10.1016\/j.asoc.2026.115158_bib0150","article-title":"Multi-task deep learning for glaucoma detection from Color Fundus images","volume":"12","author":"Pascal","year":"2022","journal-title":"Scientific Rep."},{"key":"10.1016\/j.asoc.2026.115158_bib0155","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2015","journal-title":"IEEE Conf. Comput. Vis. Pattern Recognit."},{"key":"10.1016\/j.asoc.2026.115158_bib0160","series-title":"Pattern Recognition and Computer Vision","first-page":"42","article-title":"LeViT-UNet: make faster encoders with transformer for medical image segmentation","author":"Xu","year":"2024"},{"key":"10.1016\/j.asoc.2026.115158_bib0165","series-title":"Proc. NIPS","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.asoc.2026.115158_bib0170","series-title":"Proceedings of the European Conference on Computer Vision (ECCV)","first-page":"3","article-title":"CBAM: convolutional block attention module","author":"Woo","year":"2018"},{"key":"10.1016\/j.asoc.2026.115158_bib0175","series-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","first-page":"6000","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.asoc.2026.115158_bib0180","series-title":"Medical Image Computing and Computer-Assisted Intervention (MICCAI)","first-page":"234","article-title":"U-Net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.asoc.2026.115158_bib0185","first-page":"1","article-title":"A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on danish commons","volume":"5","author":"S\u00f8rensen","year":"1948","journal-title":"Biol. Skr."},{"key":"10.1016\/j.asoc.2026.115158_bib0190","author":"Lin"},{"key":"10.1016\/j.asoc.2026.115158_bib0195","series-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"10.1016\/j.asoc.2026.115158_bib0200","series-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","first-page":"240","article-title":"Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations","author":"Sudre","year":"2017"},{"key":"10.1016\/j.asoc.2026.115158_bib0205","first-page":"228472","article-title":"A novel adversarial training framework for medical image segmentation using focal loss","volume":"8","author":"Ma","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2026.115158_bib0210","series-title":"2011 24th International Symposium on Computer-Based Medical Systems (CBMS)","first-page":"1","article-title":"RIM-ONE: an open retinal image database for optic nerve evaluation","author":"Fumero","year":"2011"},{"key":"10.1016\/j.asoc.2026.115158_bib0215","article-title":"Adam: a method for stochastic optimization","volume":"abs\/1412.6980","author":"Kingma","year":"2014","journal-title":"CoRR"},{"key":"10.1016\/j.asoc.2026.115158_bib0220","article-title":"Revisiting LARS for large batch training generalization of neural networks","volume":"6","author":"Do","year":"2024","journal-title":"IEEE Trans. Artif. Intell."},{"key":"10.1016\/j.asoc.2026.115158_bib0225","series-title":"International Joint Conference on Artificial Intelligence","article-title":"Medical image segmentation using squeeze-and-expansion transformers","author":"Li","year":"2021"},{"key":"10.1016\/j.asoc.2026.115158_bib0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.109918","article-title":"Exploring deep feature-blending capabilities to assist glaucoma screening","volume":"133","author":"Haider","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115158_bib0235","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.108347","article-title":"End-to-end multi-task learning for simultaneous optic disc and CUP segmentation and glaucoma classification in eye fundus images","volume":"116","author":"Hervella","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115158_bib0240","first-page":"1","article-title":"EARDS: EfficientNet and attention-based residual depth-wise separable convolution for joint OD and OC segmentation","volume":"17","author":"Zhou","year":"2023","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.asoc.2026.115158_bib0245","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2024.3412185","article-title":"Glaucoma identification using convolutional neural networks ensemble for optic disc and CUP segmentation","volume":"12","author":"Virbukait\u0117","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2026.115158_bib0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2024.109423","article-title":"LC-MANet: location-constrained joint optic disc and CUP segmentation via multiplex aggregation network","volume":"118","author":"Yu","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.asoc.2026.115158_bib0255","doi-asserted-by":"crossref","DOI":"10.1016\/j.patrec.2025.03.013","article-title":"PAT: pixel-wise adaptive training for long-tailed segmentation","volume":"192","author":"Do","year":"2025","journal-title":"Pattern Recognit. Lett."},{"issue":"11","key":"10.1016\/j.asoc.2026.115158_bib0260","doi-asserted-by":"crossref","first-page":"3777","DOI":"10.3390\/app10113777","article-title":"Multi-path recurrent u-net segmentation of retinal fundus image","volume":"10","author":"Jiang","year":"2020","journal-title":"Appl. Sci."},{"issue":"10","key":"10.1016\/j.asoc.2026.115158_bib0265","doi-asserted-by":"crossref","first-page":"6529","DOI":"10.1364\/BOE.434841","article-title":"GDCSeg-Net: general optic disc and CUP segmentation network for multi-device fundus images","volume":"12","author":"Zhu","year":"2021","journal-title":"Biomed. Opt. Express"},{"key":"10.1016\/j.asoc.2026.115158_bib0270","series-title":"Proceedings of the IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","first-page":"1","article-title":"Deep learning and ensemble method for optic disc and CUP segmentation","author":"Kim","year":"2022"},{"key":"10.1016\/j.asoc.2026.115158_bib0275","series-title":"Proceedings of the 23rd International Conference of the Catalan Association for Artificial Intelligence (CCIA)","first-page":"305","article-title":"Segmenting the optic disc using a deep learning ensemble model based on OWA operators","author":"Ali","year":"2021"},{"key":"10.1016\/j.asoc.2026.115158_bib0280","doi-asserted-by":"crossref","first-page":"9224","DOI":"10.34119\/bjhrv3n4-160","article-title":"Evolving convolutional neural networks for glaucoma diagnosis","volume":"3","author":"de Moura Lima","year":"2020","journal-title":"Braz. J. Health Rev."},{"key":"10.1016\/j.asoc.2026.115158_bib0285","doi-asserted-by":"crossref","first-page":"100233","DOI":"10.1016\/j.xops.2022.100233","article-title":"Detecting glaucoma from Fundus photographs using deep learning without convolutions: transformer for improved generalization","volume":"3","author":"Fan","year":"2023","journal-title":"Ophthalmol. Sci."},{"key":"10.1016\/j.asoc.2026.115158_bib0290","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102295","article-title":"Diagnosing glaucoma on imbalanced data with self-ensemble dual-curriculum learning","volume":"75","author":"Zhao","year":"2022","journal-title":"Med. Image Anal."}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S156849462600606X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S156849462600606X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T16:05:32Z","timestamp":1776873932000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S156849462600606X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":58,"alternative-id":["S156849462600606X"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115158","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"GlaucoFormer: A transformer-based model for glaucoma detection and segmentation","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115158","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"115158"}}