{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T22:35:02Z","timestamp":1793486102628,"version":"4.5.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T00:00:00Z","timestamp":1718323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T00:00:00Z","timestamp":1718323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Collaborative Innovation Project of Shanghai Institute of Technology","award":["XTCX2022-27"],"award-info":[{"award-number":["XTCX2022-27"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62175156"],"award-info":[{"award-number":["62175156"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["22S31903000"],"award-info":[{"award-number":["22S31903000"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Shenkang Hospital Clinical Research Program","award":["SHDC12020127"],"award-info":[{"award-number":["SHDC12020127"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s11517-024-03144-6","type":"journal-article","created":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T23:44:19Z","timestamp":1718322259000},"page":"3459-3469","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Multi-label classification of retinal diseases based on fundus images using Resnet and Transformer"],"prefix":"10.1007","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2946-7518","authenticated-orcid":false,"given":"Jiaqing","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianfeng","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangnan","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guogang","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cuixia","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,14]]},"reference":[{"issue":"5","key":"3144_CR1","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1001\/archophthalmol.2012.1032","volume":"130","author":"R Bourne","year":"2012","unstructured":"Bourne R, Price H, Stevens G (2012) Global burden of visual impairment and blindness. Arch Ophthalmol 130(5):645\u2013647. https:\/\/doi.org\/10.1001\/archophthalmol.2012.1032","journal-title":"Arch Ophthalmol"},{"key":"3144_CR2","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770\u20138. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"3144_CR3","doi-asserted-by":"publisher","unstructured":"Vaswani A, Shazeer NM, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al (2017) Attention is all you need. Advances in neural information processing systems 30. https:\/\/doi.org\/10.48550\/arXiv.1706.03762","DOI":"10.48550\/arXiv.1706.03762"},{"key":"3144_CR4","doi-asserted-by":"publisher","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. https:\/\/doi.org\/10.48550\/arXiv.1409.1556","DOI":"10.48550\/arXiv.1409.1556"},{"key":"3144_CR5","doi-asserted-by":"publisher","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, et al (2021) Swin transformer: hierarchical vision transformer using shifted windows. 2021 IEEE\/CVF International Conference on Computer Vision (ICCV). 9992\u201310002. https:\/\/doi.org\/10.48550\/arXiv.2103.14030","DOI":"10.48550\/arXiv.2103.14030"},{"key":"3144_CR6","doi-asserted-by":"publisher","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. ArXiv.abs\/2010.11929. https:\/\/doi.org\/10.48550\/arXiv.2010.11929","DOI":"10.48550\/arXiv.2010.11929"},{"key":"3144_CR7","doi-asserted-by":"publisher","unstructured":"He J, Li C, Ye J, Wang S, Qiao Y, Gu L (2020) Classification of ocular diseases employing attention-based unilateral and bilateral feature weighting and fusion. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). 1258\u201361. https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098525","DOI":"10.1109\/ISBI45749.2020.9098525"},{"key":"3144_CR8","doi-asserted-by":"publisher","first-page":"102608","DOI":"10.1016\/j.media.2022.102608","volume":"82","author":"C Playout","year":"2022","unstructured":"Playout C, Duval R, Boucher MC, Cheriet F (2022) Focused attention in transformers for interpretable classification of retinal images. Med Image Anal 82:102608. https:\/\/doi.org\/10.1016\/j.media.2022.102608","journal-title":"Med Image Anal"},{"key":"3144_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42600-022-00200-8","volume":"38","author":"D Rocha","year":"2022","unstructured":"Rocha D, Ferreira F, Peixoto Z (2022) Diabetic retinopathy classification using VGG16 neural network. Res Biomed Eng 38:1\u201312. https:\/\/doi.org\/10.1007\/s42600-022-00200-8","journal-title":"Res Biomed Eng"},{"key":"3144_CR10","doi-asserted-by":"publisher","unstructured":"Prawira R, Bustamam A, Anki P (2021) Multi label classification of retinal disease on fundus images using AlexNet and VGG16 architectures. In 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE,\u00a0pp. 464\u2013468.\u00a0https:\/\/doi.org\/10.1109\/ISRITI54043.2021.9702817","DOI":"10.1109\/ISRITI54043.2021.9702817"},{"key":"3144_CR11","doi-asserted-by":"publisher","first-page":"1382","DOI":"10.12928\/telkomnika.v18i3.14868","volume":"18","author":"W Setiawan","year":"2020","unstructured":"Setiawan W, Utoyo M, Rulaningtyas R (2020) Transfer learning with multiple pre-trained network for fundus classification. TELKOMNIKA (Telecommun Comput Electron Control) 18:1382. https:\/\/doi.org\/10.12928\/telkomnika.v18i3.14868","journal-title":"TELKOMNIKA (Telecommun Comput Electron Control)"},{"key":"3144_CR12","doi-asserted-by":"publisher","first-page":"167","DOI":"10.31642\/JoKMC\/2018\/100225","volume":"10","author":"R Dihin","year":"2023","unstructured":"Dihin R, Alshemmary E, Al-Jawher W (2023) Diabetic retinopathy classification using swin transformer with multi wavelet. J Kufa Math Comput 10:167\u2013172. https:\/\/doi.org\/10.31642\/JoKMC\/2018\/100225","journal-title":"J Kufa Math Comput"},{"key":"3144_CR13","doi-asserted-by":"publisher","first-page":"105821","DOI":"10.1016\/j.compbiomed.2022.105821","volume":"148","author":"Y Liu","year":"2022","unstructured":"Liu Y, Zhao J, Luo Q, Shen C, Wang R, Ding X (2022) Automated classification of cervical lymph-node-level from ultrasound using depthwise separable convolutional swin transformer. Comput Biol Med 148:105821","journal-title":"Comput Biol Med"},{"key":"3144_CR14","doi-asserted-by":"publisher","unstructured":"Mallick S, Paul J, Sengupta N, Sil J (2022) Study of different transformer based networks for glaucoma detection. In TENCON 2022-2022 IEEE Region 10 Conference (TENCON).\u00a0IEEE, pp. 1\u20136. https:\/\/doi.org\/10.1109\/TENCON55691.2022.9977730","DOI":"10.1109\/TENCON55691.2022.9977730"},{"key":"3144_CR15","doi-asserted-by":"publisher","unstructured":"Wassel M, Hamdi A, Adly N, Torki M (2022) Vision transformers based classification for glaucomatous eye condition. In 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, pp. 5082\u20135088. https:\/\/doi.org\/10.1109\/ICPR56361.2022.9956086","DOI":"10.1109\/ICPR56361.2022.9956086"},{"key":"3144_CR16","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/978-3-030-87237-3_5","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"S Yu","year":"2021","unstructured":"Yu S, Ma K, Bi Q, Bian C, Ning M, He N et al (2021) MIL-ViT: multiple instance learning enhanced vision transformer for fundus image classification. In: de Bruijne M, Cattin PC, Cotin S, Padoy N, Speidel S, Zheng Y et al (eds) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021. Springer International Publishing, Cham, pp 45\u201354"},{"key":"3144_CR17","doi-asserted-by":"publisher","unstructured":"Nagula JM, Raman M, Goel T, Roy P (2022) ViT-DR: vision transformers in diabetic retinopathy grading using fundus images. In 2022 IEEE 10th region 10 humanitarian technology conference (R10-HTC). IEEE, pp. 167\u2013172. https:\/\/doi.org\/10.1109\/R10-HTC54060.2022.9930027","DOI":"10.1109\/R10-HTC54060.2022.9930027"},{"key":"3144_CR18","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1109\/LSP.2021.3057548","volume":"28","author":"J Lin","year":"2021","unstructured":"Lin J, Cai Q, Lin M (2021) Multi-label classification of fundus images with graph convolutional network and self-supervised learning. IEEE Signal Process Lett 28:454\u2013458. https:\/\/doi.org\/10.1109\/LSP.2021.3057548","journal-title":"IEEE Signal Process Lett"},{"key":"3144_CR19","doi-asserted-by":"publisher","unstructured":"Ju L, Wang X, Yu Z, Wang L, Zhao X, Ge Z (2021) Long-tailed multi-label retinal diseases recognition using hierarchical information and hybrid knowledge distillation. arXiv preprint arXiv:2111.08913. https:\/\/doi.org\/10.48550\/arXiv.2111.08913","DOI":"10.48550\/arXiv.2111.08913"},{"key":"3144_CR20","doi-asserted-by":"publisher","unstructured":"Li C, Ye J, Junjun H, Wang S, Qiao Y, Gu L (2020) Dense correlation network for automated multi-label ocular disease detection with paired color fundus photographs. In 2020 IEEE 17th international symposium on biomedical imaging (ISBI). IEEE, pp. 1\u20134. https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098340","DOI":"10.1109\/ISBI45749.2020.9098340"},{"key":"3144_CR21","doi-asserted-by":"publisher","first-page":"212499","DOI":"10.1109\/ACCESS.2020.3040275","volume":"8","author":"J Wang","year":"2020","unstructured":"Wang J, Yang L, Huo Z, He W, Luo J (2020) Multi-label classification of fundus images with EfficientNet. IEEE Access 8:212499\u2013212508. https:\/\/doi.org\/10.1109\/ACCESS.2020.3040275","journal-title":"IEEE Access"},{"key":"3144_CR22","doi-asserted-by":"publisher","first-page":"102329","DOI":"10.1016\/j.bspc.2020.102329","volume":"66","author":"N Gour","year":"2020","unstructured":"Gour N, Khanna P (2020) Multi-class multi-label ophthalmological disease detection using transfer learning based convolutional neural network. Biomed Signal Process Control 66:102329. https:\/\/doi.org\/10.1016\/j.bspc.2020.102329","journal-title":"Biomed Signal Process Control"},{"key":"3144_CR23","doi-asserted-by":"publisher","first-page":"105909","DOI":"10.1016\/j.compbiomed.2022.105909","volume":"149","author":"K Sun","year":"2022","unstructured":"Sun K, He M, Xu Y, Wu Q, He Z, Li W et al (2022) Multi-label classification of fundus images with graph convolutional network and LightGBM. Comput Biol Med 149:105909. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105909","journal-title":"Comput Biol Med"},{"key":"3144_CR24","unstructured":"The official documentation of the standard version of ResNet101 in PyTorch. In: torchvision.models. https:\/\/pytorch.org\/vision\/stable\/models\/generated\/torchvision.models.resnet101.html#torchvision.models.ResNet101_Weights"},{"key":"3144_CR25","unstructured":"The source code of the standard version of ResNet101 in PyTorch. In: torchvision.models. https:\/\/github.com\/pytorch\/vision\/blob\/main\/torchvision\/models\/resnet.py"},{"key":"3144_CR26","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"N Carion","year":"2020","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: Vedaldi A, Bischof H, Brox T, Frahm J-M (eds) Computer Vision \u2013 ECCV 2020. Springer International Publishing, Cham, pp 213\u2013229"},{"key":"3144_CR27","unstructured":"Peking University International Competition on Ocular Disease Intelligent Recognition (ODIR-2019), https:\/\/odir2019.grand-challenge.org"},{"key":"3144_CR28","unstructured":"Retinal image analysis for multi-disease detection. https:\/\/riadd.grand-challenge.org\/Home\/. Accessed: 2022\u201307\u20134"},{"key":"3144_CR29","doi-asserted-by":"publisher","unstructured":"Xu K, Ba J, Kiros R, Cho K, Courville AC, Salakhutdinov R, et al (2015) Show, attend and tell: neural image caption generation with visual attention. Int Conf Mach Learn. In International conference on machine learning. PMLR, pp. 2048\u20132057. https:\/\/doi.org\/10.48550\/arXiv.1502.03044","DOI":"10.48550\/arXiv.1502.03044"},{"key":"3144_CR30","doi-asserted-by":"publisher","unstructured":"Nam H, Ha J-W, Kim J (2016) Dual attention networks for multimodal reasoning and matching. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2156\u201364. https:\/\/doi.org\/10.48550\/arXiv.1611.00471","DOI":"10.48550\/arXiv.1611.00471"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03144-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-024-03144-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03144-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:13:38Z","timestamp":1729037618000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-024-03144-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,14]]},"references-count":30,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["3144"],"URL":"https:\/\/doi.org\/10.1007\/s11517-024-03144-6","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,14]]},"assertion":[{"value":"24 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This is an observational study. The publishing of this dataset follows the ethical and privacy rules of China.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The authors affirm that human research participants provided informed consent for the publication of the dataset images.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}