{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:58:10Z","timestamp":1774123090848,"version":"3.50.1"},"reference-count":41,"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":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.bspc.2026.110133","type":"journal-article","created":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T16:31:10Z","timestamp":1774110670000},"page":"110133","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PB","title":["Dynamic domain-adaptive collaborative learning for diabetic retinopathy grading"],"prefix":"10.1016","volume":"120","author":[{"given":"Zhuoqun","family":"Xia","sequence":"first","affiliation":[]},{"given":"Lan","family":"Pu","sequence":"additional","affiliation":[]},{"given":"Suili","family":"Ding","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0598-7698","authenticated-orcid":false,"given":"Jingjing","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Yicong","family":"Shu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"9","key":"10.1016\/j.bspc.2026.110133_b1","doi-asserted-by":"crossref","first-page":"1677","DOI":"10.1016\/S0161-6420(03)00475-5","article-title":"Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales","volume":"110","author":"Wilkinson","year":"2003","journal-title":"Ophthalmology"},{"issue":"2","key":"10.1016\/j.bspc.2026.110133_b2","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1007\/s13246-021-01012-3","article-title":"Diabetic retinopathy classification based on multipath CNN and machine learning classifiers","volume":"44","author":"Gayathri","year":"2021","journal-title":"Phys. Eng. Sci. Med."},{"issue":"4","key":"10.1016\/j.bspc.2026.110133_b3","first-page":"714","article-title":"Diabetic retinopathy grading based on deep convolutional neural networks","volume":"13","author":"Zhang","year":"2023","journal-title":"J. Med. Imaging Health Inform."},{"key":"10.1016\/j.bspc.2026.110133_b4","first-page":"M9","article-title":"Earliest diabetic retinopathy classification using deep convolution neural networks. pdf","volume":"10","author":"Sankar","year":"2016","journal-title":"Int. J. Adv. Eng. Technol"},{"key":"10.1016\/j.bspc.2026.110133_b5","article-title":"Automated detection of diabetic retinopathy using fluorescein angiography photographs","author":"Alban","year":"2016","journal-title":"Rep. Standford Educ."},{"key":"10.1016\/j.bspc.2026.110133_b6","first-page":"1","article-title":"A cross-lesion attention network for accurate diabetic retinopathy grading with fundus images","volume":"72","author":"Liu","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"5","key":"10.1016\/j.bspc.2026.110133_b7","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1109\/TMI.2019.2951844","article-title":"Canet: Cross-disease attention network for joint diabetic retinopathy and diabetic macular edema grading","volume":"39","author":"Li","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.bspc.2026.110133_b8","first-page":"11845","article-title":"Vision transformer adapter-based hyperbolic embeddings for multi-lesion segmentation in diabetic retinopathy","volume":"13","author":"Wang","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bspc.2026.110133_b9","series-title":"Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III 20","first-page":"533","article-title":"Lesion detection and grading of diabetic retinopathy via two-stages deep convolutional neural networks","author":"Yang","year":"2017"},{"key":"10.1016\/j.bspc.2026.110133_b10","doi-asserted-by":"crossref","unstructured":"R. Sun, Y. Li, T. Zhang, Z. Mao, F. Wu, Y. Zhang, Lesion-Aware Transformers for Diabetic Retinopathy Grading, in: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2021, pp. 10933\u201310942.","DOI":"10.1109\/CVPR46437.2021.01079"},{"issue":"8","key":"10.1016\/j.bspc.2026.110133_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.jksuci.2023.101719","article-title":"A comprehensive computer-aided system for an early-stage diagnosis and classification of diabetic macular edema","volume":"35","author":"Zubair","year":"2023","journal-title":"J. King Saud Univ. \u2013 Comput. Inf. Sci."},{"key":"10.1016\/j.bspc.2026.110133_b12","first-page":"239","article-title":"Automated detection of optic disc for the analysis of retina using color fundus image","author":"Zubair","year":"2013","journal-title":"Proc. 2013 Int. Conf. Signal, Image Process. Appl. (ICSIA)"},{"key":"10.1016\/j.bspc.2026.110133_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2025.102559","article-title":"Enhanced glaucoma classification through advanced segmentation by integrating cup-to-disc ratio and neuro-retinal rim features","volume":"123","author":"Pannu","year":"2025","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.bspc.2026.110133_b14","article-title":"A comprehensive review tracing the evolution of volumetric medical imaging analysis from classic CNNs to emerging AI-agents","author":"Owais","year":"2025","journal-title":"TechRxiv"},{"issue":"1","key":"10.1016\/j.bspc.2026.110133_b15","doi-asserted-by":"crossref","first-page":"22533","DOI":"10.1038\/s41598-024-73823-9","article-title":"Enhanced gastric cancer classification and quantification interpretable framework using digital histopathology images","volume":"14","author":"Zubair","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bspc.2026.110133_b16","first-page":"1","article-title":"Automated grading of diabetic macular edema using color retinal photographs","author":"Zubair","year":"2022","journal-title":"Proc. 2022 2nd Int. Conf. Smart Syst. Emerg. Technol. (SMARTTECH)"},{"issue":"2","key":"10.1016\/j.bspc.2026.110133_b17","first-page":"187","article-title":"Classification of diabetic macular edema and its stages using color fundus image","volume":"12","author":"Zubair","year":"2014","journal-title":"J. Electron. Sci. Technol."},{"key":"10.1016\/j.bspc.2026.110133_b18","first-page":"1","article-title":"Multiclass classification of retinal disorders using optical coherence tomography images","author":"Shahzadi","year":"2024","journal-title":"Proc. 2024 Horizons Inf. Technol. Eng. (HITE)"},{"key":"10.1016\/j.bspc.2026.110133_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2025.109014","article-title":"A comprehensive review of techniques, algorithms, advancements, challenges, and clinical applications of multi-modal medical image fusion for improved diagnosis","volume":"272","author":"Zubair","year":"2025","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.bspc.2026.110133_b20","doi-asserted-by":"crossref","unstructured":"Y. Zhou, X. He, L. Huang, L. Liu, F. Zhu, S. Cui, L. Shao, Collaborative Learning of Semi-Supervised Segmentation and Classification for Medical Images, in: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2019, pp. 2074\u20132083.","DOI":"10.1109\/CVPR.2019.00218"},{"issue":"11","key":"10.1016\/j.bspc.2026.110133_b21","doi-asserted-by":"crossref","first-page":"11407","DOI":"10.1109\/TCYB.2021.3062638","article-title":"Robust collaborative learning of patch-level and image-level annotations for diabetic retinopathy grading from fundus image","volume":"52","author":"Yang","year":"2022","journal-title":"IEEE Trans. Cybern."},{"issue":"11","key":"10.1016\/j.bspc.2026.110133_b22","doi-asserted-by":"crossref","first-page":"3445","DOI":"10.1109\/TMI.2022.3186698","article-title":"Dual adversarial attention mechanism for unsupervised domain adaptive medical image segmentation","volume":"41","author":"Chen","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110133_b23","doi-asserted-by":"crossref","first-page":"7834","DOI":"10.1109\/TIP.2020.3006377","article-title":"Collaborative unsupervised domain adaptation for medical image diagnosis","volume":"29","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.bspc.2026.110133_b24","series-title":"KI 2021: Advances in Artificial Intelligence: 44th German Conference on AI, Virtual Event, September 27\u2013October 1, 2021, Proceedings 44","first-page":"349","article-title":"Self-supervised domain adaptation for diabetic retinopathy grading using vessel image reconstruction","author":"Nguyen","year":"2021"},{"key":"10.1016\/j.bspc.2026.110133_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105341","article-title":"Collaborative learning of weakly-supervised domain adaptation for diabetic retinopathy grading on retinal images","volume":"144","author":"Cao","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110133_b26","doi-asserted-by":"crossref","unstructured":"J.-Y. Zhu, T. Park, P. Isola, A.A. Efros, Unpaired image-to-image translation using cycle-consistent adversarial networks, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 2223\u20132232.","DOI":"10.1109\/ICCV.2017.244"},{"issue":"5","key":"10.1016\/j.bspc.2026.110133_b27","doi-asserted-by":"crossref","first-page":"2216","DOI":"10.1109\/JBHI.2021.3119519","article-title":"Joint learning of multi-level tasks for diabetic retinopathy grading on low-resolution fundus images","volume":"26","author":"Wang","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"3","key":"10.1016\/j.bspc.2026.110133_b28","doi-asserted-by":"crossref","first-page":"25","DOI":"10.3390\/data3030025","article-title":"Indian diabetic retinopathy image dataset (idrid): a database for diabetic retinopathy screening research","volume":"3","author":"Porwal","year":"2018","journal-title":"Data"},{"key":"10.1016\/j.bspc.2026.110133_b29","doi-asserted-by":"crossref","first-page":"231","DOI":"10.5566\/ias.1155","article-title":"Feedback on a publicly distributed image database: the messidor database","author":"Decenci\u00e8re","year":"2014","journal-title":"Image Anal. Stereol."},{"key":"10.1016\/j.bspc.2026.110133_b30","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.ins.2019.06.011","article-title":"Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening","volume":"501","author":"Li","year":"2019","journal-title":"Inform. Sci."},{"key":"10.1016\/j.bspc.2026.110133_b31","series-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"10.1016\/j.bspc.2026.110133_b32","series-title":"2016 IEEE International Symposium on Multimedia","first-page":"209","article-title":"New deep neural nets for fine-grained diabetic retinopathy recognition on hybrid color space","author":"Vo","year":"2016"},{"key":"10.1016\/j.bspc.2026.110133_b33","series-title":"Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III 20","first-page":"267","article-title":"Zoom-in-net: Deep mining lesions for diabetic retinopathy detection","author":"Wang","year":"2017"},{"issue":"1","key":"10.1016\/j.bspc.2026.110133_b34","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1109\/TMI.2020.3023463","article-title":"Cabnet: Category attention block for imbalanced diabetic retinopathy grading","volume":"40","author":"He","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110133_b35","series-title":"2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","first-page":"2724","article-title":"Multi-cell multi-task convolutional neural networks for diabetic retinopathy grading","author":"Zhou","year":"2018"},{"key":"10.1016\/j.bspc.2026.110133_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.107993","article-title":"CRA-net: Transformer guided category-relation attention network for diabetic retinopathy grading","volume":"170","author":"Zang","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110133_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123523","article-title":"A deep integrative approach for diabetic retinopathy classification with synergistic channel-spatial and self-attention mechanism","volume":"249","author":"Madarapu","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.bspc.2026.110133_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2024.102782","article-title":"An interpretable dual attention network for diabetic retinopathy grading: Idanet","volume":"149","author":"Bhati","year":"2024","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.bspc.2026.110133_b39","doi-asserted-by":"crossref","unstructured":"C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the inception architecture for computer vision, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2818\u20132826.","DOI":"10.1109\/CVPR.2016.308"},{"key":"10.1016\/j.bspc.2026.110133_b40","doi-asserted-by":"crossref","unstructured":"G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, Densely connected convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"key":"10.1016\/j.bspc.2026.110133_b41","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426006877?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426006877?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:25:10Z","timestamp":1774121110000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426006877"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":41,"alternative-id":["S1746809426006877"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110133","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Dynamic domain-adaptive collaborative learning for diabetic retinopathy grading","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110133","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110133"}}