{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:20:45Z","timestamp":1770747645642,"version":"3.49.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T00:00:00Z","timestamp":1724889600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T00:00:00Z","timestamp":1724889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s13042-024-02358-2","type":"journal-article","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T17:02:43Z","timestamp":1724950963000},"page":"1661-1669","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhancing ocular diseases recognition with domain adaptive framework: leveraging domain confusion"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4133-8181","authenticated-orcid":false,"given":"Zayn","family":"Wang","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,29]]},"reference":[{"key":"2358_CR1","doi-asserted-by":"publisher","unstructured":"Bousmalis K, Trigeorgis G, Silberman N, et al (2016) Domain separation networks. Advances in neural information processing systems 29. https:\/\/doi.org\/10.48550\/arXiv.1608.06019","DOI":"10.48550\/arXiv.1608.06019"},{"key":"2358_CR2","doi-asserted-by":"publisher","unstructured":"Bousmalis K, Silberman N, Dohan D, et al (2017) Unsupervised pixel-level domain adaptation with generative adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 95\u2013104, https:\/\/doi.org\/10.1109\/CVPR.2017.18","DOI":"10.1109\/CVPR.2017.18"},{"issue":"4","key":"2358_CR3","doi-asserted-by":"publisher","first-page":"e489","DOI":"10.1016\/S2214-109X(20)30488-5","volume":"9","author":"MJ Burton","year":"2021","unstructured":"Burton MJ, Ramke J, Marques AP et al (2021) The lancet global health commission on global eye health: Vision beyond 2020. Lancet Glob Health 9(4):e489\u2013e551. https:\/\/doi.org\/10.1016\/S2214-109X(20)30488-5","journal-title":"Lancet Glob Health"},{"issue":"4","key":"2358_CR4","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1118\/1.596065","volume":"14","author":"HP Chan","year":"1987","unstructured":"Chan HP, Doi K, Galhotra S et al (1987) Image feature analysis and computer-aided diagnosis in digital radiography. i. automated detection of microcalcifications in mammography: Image feature analysis. i. microcalcification detection. Med Phys 14(4):538\u2013548. https:\/\/doi.org\/10.1118\/1.596065","journal-title":"Med Phys"},{"issue":"10","key":"2358_CR5","doi-asserted-by":"publisher","first-page":"1102","DOI":"10.1097\/00004424-199010000-00006","volume":"25","author":"HP Chan","year":"1990","unstructured":"Chan HP, Doi K, Vybrony CJ et al (1990) Improvement in radiologists?? detection of clustered microcalcifications on mammograms: The potential of computer-aided diagnosis. Invest Radiol 25(10):1102\u20131110. https:\/\/doi.org\/10.1097\/00004424-199010000-00006","journal-title":"Invest Radiol"},{"key":"2358_CR6","doi-asserted-by":"publisher","unstructured":"Chen Y, Li W, Sakaridis C, et al (2018) Domain adaptive faster r-cnn for object detection in the wild. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. IEEE, pp 3339\u20133348, https:\/\/doi.org\/10.1109\/CVPR.2018.00352","DOI":"10.1109\/CVPR.2018.00352"},{"issue":"6","key":"2358_CR7","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1038\/mp.2013.78","volume":"19","author":"A Di Martino","year":"2014","unstructured":"Di Martino A, Yan CG, Li Q et al (2014) The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry 19(6):659\u2013667. https:\/\/doi.org\/10.1038\/mp.2013.78","journal-title":"Mol Psychiatry"},{"issue":"4\u20135","key":"2358_CR8","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.compmedimag.2007.02.002","volume":"31","author":"K Doi","year":"2007","unstructured":"Doi K (2007) Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Comput Med Imaging Graph 31(4\u20135):198\u2013211. https:\/\/doi.org\/10.1016\/j.compmedimag.2007.02.002","journal-title":"Comput Med Imaging Graph"},{"key":"2358_CR9","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.neucom.2018.09.013","volume":"321","author":"M Frid-Adar","year":"2018","unstructured":"Frid-Adar M, Diamant I, Klang E et al (2018) Gan-based synthetic medical image augmentation for increased cnn performance in liver lesion classification. Neurocomputing 321:321\u2013331. https:\/\/doi.org\/10.1016\/j.neucom.2018.09.013","journal-title":"Neurocomputing"},{"key":"2358_CR10","doi-asserted-by":"publisher","unstructured":"Ganin Y, Lempitsky V (2015) Unsupervised domain adaptation by backpropagation. https:\/\/doi.org\/10.48550\/arXiv.1409.7495","DOI":"10.48550\/arXiv.1409.7495"},{"key":"2358_CR11","doi-asserted-by":"publisher","unstructured":"Ganin Y, Ustinova E, Ajakan H, et al (2017) Domain-adversarial training of neural networks. In: Csurka G (ed) Domain Adaptation in Computer Vision Applications. Springer International Publishing, p 189\u2013209, https:\/\/doi.org\/10.1007\/978-3-319-58347-1_10","DOI":"10.1007\/978-3-319-58347-1_10"},{"key":"2358_CR12","doi-asserted-by":"publisher","unstructured":"Ghiasi G, Lin TY, Le QV (2018) Dropblock: A regularization method for convolutional networks. Advances in neural information processing systems 31. https:\/\/doi.org\/10.48550\/arXiv.1810.12890","DOI":"10.48550\/arXiv.1810.12890"},{"issue":"10","key":"2358_CR13","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","volume":"38","author":"Z Gu","year":"2019","unstructured":"Gu Z, Cheng J, Fu H et al (2019) Ce-net: Context encoder network for 2d medical image segmentation. IEEE Trans Med Imaging 38(10):2281\u20132292. https:\/\/doi.org\/10.1109\/TMI.2019.2903562","journal-title":"IEEE Trans Med Imaging"},{"issue":"3","key":"2358_CR14","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1109\/TBME.2021.3117407","volume":"69","author":"H Guan","year":"2022","unstructured":"Guan H, Liu M (2022) Domain adaptation for medical image analysis: a survey. IEEE Trans Biomed Eng 69(3):1173\u20131185. https:\/\/doi.org\/10.1109\/TBME.2021.3117407","journal-title":"IEEE Trans Biomed Eng"},{"key":"2358_CR15","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S et al (2015). Deep residual learning for image recognition. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"2358_CR16","doi-asserted-by":"publisher","unstructured":"Hoffman J, Tzeng E, Park T, et al (2018) Cycada: Cycle-consistent adversarial domain adaptation. In: International Conference on Machine Learning, pp 1989\u20131998, https:\/\/doi.org\/10.48550\/arXiv.1711.03213","DOI":"10.48550\/arXiv.1711.03213"},{"key":"2358_CR17","doi-asserted-by":"publisher","unstructured":"Hsu HK, Yao CH, Tsai YH, et al (2019) Progressive domain adaptation for object detection. https:\/\/doi.org\/10.48550\/arXiv.1910.11319","DOI":"10.48550\/arXiv.1910.11319"},{"key":"2358_CR18","doi-asserted-by":"publisher","unstructured":"Li N, Li T, Hu C, et al (2021) A benchmark of ocular disease intelligent recognition: One shot for multi-disease detection. In: Wolf F, Gao W (eds) Benchmarking, Measuring, and Optimizing, vol 12614. Springer International Publishing, p 177\u2013193, https:\/\/doi.org\/10.1007\/978-3-030-71058-3_11","DOI":"10.1007\/978-3-030-71058-3_11"},{"key":"2358_CR19","doi-asserted-by":"publisher","unstructured":"Li Q, Cai W, Wang X, et al (2014) Medical image classification with convolutional neural network. In: 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV). IEEE, pp 844\u2013848, https:\/\/doi.org\/10.1109\/ICARCV.2014.7064414","DOI":"10.1109\/ICARCV.2014.7064414"},{"key":"2358_CR20","doi-asserted-by":"publisher","unstructured":"Lin TY, Goyal P, Girshick R, et al (2017) Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp 2980\u20132988, https:\/\/doi.org\/10.1109\/ICCV.2017.324","DOI":"10.1109\/ICCV.2017.324"},{"issue":"9","key":"2358_CR21","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.1162\/jocn.2007.19.9.1498","volume":"19","author":"DS Marcus","year":"2007","unstructured":"Marcus DS, Wang TH, Parker J et al (2007) Open access series of imaging studies (oasis): Cross-sectional mri data in young, middle aged, nondemented, and demented older adults. J Cogn Neurosci 19(9):1498\u20131507. https:\/\/doi.org\/10.1162\/jocn.2007.19.9.1498","journal-title":"J Cogn Neurosci"},{"key":"2358_CR22","doi-asserted-by":"publisher","unstructured":"Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV). IEEE, pp 565\u2013571, https:\/\/doi.org\/10.1109\/3DV.2016.79","DOI":"10.1109\/3DV.2016.79"},{"issue":"3","key":"2358_CR23","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1109\/TMI.2003.809588","volume":"22","author":"A Pizurica","year":"2003","unstructured":"Pizurica A, Philips W, Lemahieu I et al (2003) A versatile wavelet domain noise filtration technique for medical imaging. IEEE Trans Med Imaging 22(3):323\u2013331. https:\/\/doi.org\/10.1109\/TMI.2003.809588","journal-title":"IEEE Trans Med Imaging"},{"issue":"8","key":"2358_CR24","doi-asserted-by":"publisher","first-page":"2311","DOI":"10.1007\/s11136-021-02817-1","volume":"30","author":"PKM Purola","year":"2021","unstructured":"Purola PKM, N\u00e4ttinen JE, Ojamo MUI et al (2021) Prevalence and 11-year incidence of common eye diseases and their relation to health-related quality of life, mental health, and visual impairment. Qual Life Res 30(8):2311\u20132327. https:\/\/doi.org\/10.1007\/s11136-021-02817-1","journal-title":"Qual Life Res"},{"issue":"5","key":"2358_CR25","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"HC Shin","year":"2016","unstructured":"Shin HC, Roth HR, Gao M et al (2016) Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285\u20131298. https:\/\/doi.org\/10.1109\/TMI.2016.2528162","journal-title":"IEEE Trans Med Imaging"},{"issue":"3","key":"2358_CR26","doi-asserted-by":"publisher","first-page":"1638","DOI":"10.1364\/BOE.8.001638","volume":"8","author":"EA Swanson","year":"2017","unstructured":"Swanson EA, Fujimoto JG (2017) The ecosystem that powered the translation of oct from fundamental research to clinical and commercial impact [invited]. Biomed Opt Express 8(3):1638. https:\/\/doi.org\/10.1364\/BOE.8.001638","journal-title":"Biomed Opt Express"},{"key":"2358_CR27","doi-asserted-by":"publisher","unstructured":"Torralba A, Efros AA (2011) Unbiased look at dataset bias. In: CVPR 2011. IEEE, pp 1521\u20131528, https:\/\/doi.org\/10.1109\/CVPR.2011.5995347","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"2358_CR28","doi-asserted-by":"publisher","unstructured":"Tsai YH, Hung WC, Schulter S, et al (2018) Learning to adapt structured output space for semantic segmentation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. IEEE, pp 7472\u20137481, https:\/\/doi.org\/10.1109\/CVPR.2018.00780","DOI":"10.1109\/CVPR.2018.00780"},{"key":"2358_CR29","doi-asserted-by":"publisher","unstructured":"Tzeng E, Hoffman J, Zhang N, et al (2014) Deep domain confusion: Maximizing for domain invariance. https:\/\/doi.org\/10.48550\/arXiv.1412.3474","DOI":"10.48550\/arXiv.1412.3474"},{"key":"2358_CR30","doi-asserted-by":"publisher","unstructured":"Tzeng E, Hoffman J, Saenko K, et al (2017) Adversarial discriminative domain adaptation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 2962\u20132971, https:\/\/doi.org\/10.1109\/CVPR.2017.316","DOI":"10.1109\/CVPR.2017.316"},{"key":"2358_CR31","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1038\/35067550","volume":"2","author":"F Varela","year":"2001","unstructured":"Varela F, Lachaux JP, Rodriguez E et al (2001) The brainweb: phase synchronization and large-scale integration. Nat Rev Neurosci 2:229\u2013239. https:\/\/doi.org\/10.1038\/35067550","journal-title":"Nat Rev Neurosci"},{"key":"2358_CR32","doi-asserted-by":"publisher","unstructured":"Wang Z (2022) Emotion recognition based on multi-scale convolutional neural network. In: Tan Y, Shi Y (eds) Data Mining and Big Data, vol 1744. Springer Nature Singapore, p 152\u2013164, https:\/\/doi.org\/10.1007\/978-981-19-9297-1_12","DOI":"10.1007\/978-981-19-9297-1_12"},{"key":"2358_CR33","doi-asserted-by":"publisher","unstructured":"Zhu JY, Park T, Isola P et al (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, pp 2242\u20132251, https:\/\/doi.org\/10.1109\/ICCV.2017.244","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02358-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02358-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02358-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T18:26:47Z","timestamp":1739989607000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02358-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,29]]},"references-count":33,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["2358"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02358-2","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,29]]},"assertion":[{"value":"3 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 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":"The authors have no Conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors have no relevant financial or non-financial interests to disclose. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Financial interest or Non-financial interest"}}]}}