{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T18:21:13Z","timestamp":1780424473362,"version":"3.54.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T00:00:00Z","timestamp":1677801600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T00:00:00Z","timestamp":1677801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100010905","name":"Major Research Plan","doi-asserted-by":"publisher","award":["62172103"],"award-info":[{"award-number":["62172103"]}],"id":[{"id":"10.13039\/501100010905","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010905","name":"Major Research Plan","doi-asserted-by":"publisher","award":["61876016"],"award-info":[{"award-number":["61876016"]}],"id":[{"id":"10.13039\/501100010905","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010905","name":"Major Research Plan","doi-asserted-by":"publisher","award":["62076007"],"award-info":[{"award-number":["62076007"]}],"id":[{"id":"10.13039\/501100010905","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s13042-023-01806-9","type":"journal-article","created":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T11:02:36Z","timestamp":1677841356000},"page":"149-159","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Pathological image super-resolution using mix-attention generative adversarial network"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1543-6889","authenticated-orcid":false,"given":"Zhineng","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caiyan","family":"Jia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiongjun","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,3,3]]},"reference":[{"key":"1806_CR1","unstructured":"Umer RM, Micheloni C (2021) Rbsricnn: Raw burst super-resolution through iterative convolutional neural network. arXiv preprint arXiv:2110.13217"},{"issue":"1s","key":"1806_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3231742","volume":"15","author":"Z Chen","year":"2019","unstructured":"Chen Z, Ai S, Jia C (2019) Structure-aware deep learning for product image classification. ACM Trans Multimed Comput, Commun, Appl (TOMM) 15(1s):1\u201320","journal-title":"ACM Trans Multimed Comput, Commun, Appl (TOMM)"},{"issue":"5","key":"1806_CR3","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1007\/s13042-017-0765-6","volume":"10","author":"AB Deshmukh","year":"2019","unstructured":"Deshmukh AB, Usha Rani N (2019) Fractional-grey wolf optimizer-based kernel weighted regression model for multi-view face video super resolution. Int J Mach Learn Cybern 10(5):859\u2013877","journal-title":"Int J Mach Learn Cybern"},{"issue":"4","key":"1806_CR4","doi-asserted-by":"publisher","first-page":"1699","DOI":"10.1109\/TPAMI.2020.3029425","volume":"44","author":"W Zuxuan","year":"2022","unstructured":"Zuxuan W, Li H, Xiong C, Jiang Y-G, Davis LS (2022) A dynamic frame selection framework for fast video recognition. IEEE Trans Pattern Anal Mach Intell 44(4):1699\u20131711","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"1806_CR5","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.irbm.2020.08.004","volume":"42","author":"Y Li","year":"2021","unstructured":"Li Y, Sixou B, Peyrin F (2021) A review of the deep learning methods for medical images super resolution problems. IRBM 42(2):120\u2013133","journal-title":"IRBM"},{"issue":"10","key":"1806_CR6","doi-asserted-by":"publisher","first-page":"2685","DOI":"10.1109\/TMI.2020.3046672","volume":"40","author":"C Liu","year":"2020","unstructured":"Liu C, Xie H, Zhang Y (2020) Self-supervised attention mechanism for pediatric bone age assessment with efficient weak annotation. IEEE Trans Med Imaging 40(10):2685\u20132697","journal-title":"IEEE Trans Med Imaging"},{"issue":"12","key":"1806_CR7","doi-asserted-by":"publisher","first-page":"3762","DOI":"10.1109\/TMI.2021.3097355","volume":"40","author":"X Jingyuan","year":"2021","unstructured":"Jingyuan X, Xie H, Liu C, Yang F, Zhang S, Chen X, Zhang Y (2021) Hip landmark detection with dependency mining in ultrasound image. IEEE Trans Med Imaging 40(12):3762\u20133774","journal-title":"IEEE Trans Med Imaging"},{"issue":"12","key":"1806_CR8","doi-asserted-by":"publisher","first-page":"3944","DOI":"10.1109\/TMI.2020.3008382","volume":"39","author":"C Liu","year":"2020","unstructured":"Liu C, Xie H, Zhang S, Mao Z, Sun J, Zhang Y (2020) Misshapen pelvis landmark detection with local-global feature learning for diagnosing developmental dysplasia of the hip. IEEE Trans Med Imaging 39(12):3944\u20133954","journal-title":"IEEE Trans Med Imaging"},{"key":"1806_CR9","doi-asserted-by":"crossref","unstructured":"Upadhyay U, Awate SP (2019) A mixed-supervision multilevel gan framework for image quality enhancement. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, p 556\u2013564","DOI":"10.1007\/978-3-030-32254-0_62"},{"key":"1806_CR10","doi-asserted-by":"crossref","unstructured":"Chen Z, Guo X, Yang C, Ibragimov B, Yuan Y (2020) Joint spatial-wavelet dual-stream network for super-resolution. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, p 184\u2013193","DOI":"10.1007\/978-3-030-59722-1_18"},{"key":"1806_CR11","doi-asserted-by":"publisher","first-page":"101938","DOI":"10.1016\/j.media.2020.101938","volume":"68","author":"B Li","year":"2021","unstructured":"Li B, Keikhosravi A, Loeffler AG, Eliceiri KW (2021) Single image super-resolution for whole slide image using convolutional neural networks and self-supervised color normalization. Med Image Anal 68:101938","journal-title":"Med Image Anal"},{"issue":"5","key":"1806_CR12","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1016\/j.humpath.2009.08.026","volume":"41","author":"A Huisman","year":"2010","unstructured":"Huisman A, Looijen A, van den Brink SM, van Diest PJ (2010) Creation of a fully digital pathology slide archive by high-volume tissue slide scanning. Human Pathol 41(5):751\u2013757","journal-title":"Human Pathol"},{"issue":"1","key":"1806_CR13","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1146\/annurev-pathol-011811-120902","volume":"8","author":"F Ghaznavi","year":"2013","unstructured":"Ghaznavi F, Evans A, Madabhushi A, Feldman M (2013) Digital imaging in pathology: whole-slide imaging and beyond. Annu Rev Pathol 8(1):331\u2013359","journal-title":"Annu Rev Pathol"},{"issue":"2","key":"1806_CR14","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2015","unstructured":"Dong C, Loy CC, He K, Tang X (2015) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38(2):295\u2013307","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1806_CR15","doi-asserted-by":"crossref","unstructured":"Kim J, Lee JK, Lee KM (2016) Deeply-recursive convolutional network for image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p 1637\u20131645","DOI":"10.1109\/CVPR.2016.181"},{"key":"1806_CR16","doi-asserted-by":"crossref","unstructured":"Zhang Y, Li K, Li K, Wang L, Zhong B, Fu Y (2018) Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European conference on computer vision (ECCV), p 286\u2013301","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"1806_CR17","doi-asserted-by":"crossref","unstructured":"Lee W, Lee J, Kim D, Ham B (2020) Learning with privileged information for efficient image super-resolution. In: European Conference on Computer Vision. Springer, p 465\u2013482","DOI":"10.1007\/978-3-030-58586-0_28"},{"key":"1806_CR18","doi-asserted-by":"crossref","unstructured":"Ledig C, Theis L, Husz\u00e1r F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z, et\u00a0al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p 4681\u20134690","DOI":"10.1109\/CVPR.2017.19"},{"key":"1806_CR19","doi-asserted-by":"crossref","unstructured":"Zhang W, Liu Y, Dong C, Qiao Y (2019) Ranksrgan: Generative adversarial networks with ranker for image super-resolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, p 3096\u20133105","DOI":"10.1109\/ICCV.2019.00319"},{"key":"1806_CR20","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.compmedimag.2018.10.005","volume":"71","author":"D Mahapatra","year":"2019","unstructured":"Mahapatra D, Bozorgtabar B, Garnavi R (2019) Image super-resolution using progressive generative adversarial networks for medical image analysis. Comput Med Imaging Graph 71:30\u201339","journal-title":"Comput Med Imaging Graph"},{"key":"1806_CR21","doi-asserted-by":"crossref","unstructured":"Deng Y, Feng M, Jiang Y, Zhou Y, Qing H, Xiang F, Wang Y, Bao J, Bu H (2020) Development of pathological super-resolution images using artificial intelligence based on whole slide image","DOI":"10.21203\/rs.2.24125\/v1"},{"issue":"12","key":"1806_CR22","doi-asserted-by":"publisher","first-page":"126003","DOI":"10.1117\/1.JBO.24.12.126003","volume":"24","author":"L Mukherjee","year":"2019","unstructured":"Mukherjee L, Bui HD, Keikhosravi A, Loeffler A, Eliceiri KW (2019) Super-resolution recurrent convolutional neural networks for learning with multi-resolution whole slide images. J Biomed Opt 24(12):126003","journal-title":"J Biomed Opt"},{"issue":"2","key":"1806_CR23","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1038\/s41592-020-01048-5","volume":"18","author":"C Qiao","year":"2021","unstructured":"Qiao C, Li D, Guo Y, Liu C, Jiang T, Dai Q, Li D (2021) Evaluation and development of deep neural networks for image super-resolution in optical microscopy. Nat Methods 18(2):194\u2013202","journal-title":"Nat Methods"},{"key":"1806_CR24","doi-asserted-by":"crossref","unstructured":"Zhang K, Zuo W, Zhang L (2018) Learning a single convolutional super-resolution network for multiple degradations. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p 3262\u20133271","DOI":"10.1109\/CVPR.2018.00344"},{"key":"1806_CR25","doi-asserted-by":"crossref","unstructured":"Cai J, Zeng H, Yong H, Cao Z, Zhang L (2019) Toward real-world single image super-resolution: A new benchmark and a new model. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 3086\u20133095","DOI":"10.1109\/ICCV.2019.00318"},{"key":"1806_CR26","unstructured":"Li X, Hu X, Yang J (2019) Spatial group-wise enhance: Improving semantic feature learning in convolutional networks. arXiv preprint arXiv:1905.09646,"},{"key":"1806_CR27","doi-asserted-by":"crossref","unstructured":"Liu J, Zhang W, Tang Y, Tang J, Wu G (2020) Residual feature aggregation network for image super-resolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, p 2359\u20132368","DOI":"10.1109\/CVPR42600.2020.00243"},{"key":"1806_CR28","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1806_CR29","unstructured":"Bejnordi BE, Veta M, Van Diest PJ, Van Ginneken B, Karssemeijer N, Litjens G, Van Der Jeroen AWM, Laak MH, Manson Quirine F, Balkenhol M et al (2000) Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318"},{"key":"1806_CR30","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H, Nah S, Mu\u00a0Lee K (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, p 136\u2013144","DOI":"10.1109\/CVPRW.2017.151"},{"key":"1806_CR31","doi-asserted-by":"crossref","unstructured":"Tai Y, Yang J, Liu X (2017) Image super-resolution via deep recursive residual network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p 3147\u20133155","DOI":"10.1109\/CVPR.2017.298"},{"key":"1806_CR32","first-page":"3499","volume":"33","author":"S Zhou","year":"2020","unstructured":"Zhou S, Zhang J, Zuo W, Loy CC (2020) Cross-scale internal graph neural network for image super-resolution. Adv Neural Inf Process Syst 33:3499\u20133509","journal-title":"Adv Neural Inf Process Syst"},{"key":"1806_CR33","doi-asserted-by":"crossref","unstructured":"Ma X, Guo J, Tang S, Qiao Z, Chen Q, Yang Q, Fu S (2020) Dcanet: Learning connected attentions for convolutional neural networks. arXiv preprint arXiv:2007.05099","DOI":"10.1109\/ICME51207.2021.9428397"},{"key":"1806_CR34","doi-asserted-by":"crossref","unstructured":"Mei Y, Fan Y, Zhou Y (2021) Image super-resolution with non-local sparse attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, p 3517\u20133526","DOI":"10.1109\/CVPR46437.2021.00352"},{"key":"1806_CR35","doi-asserted-by":"crossref","unstructured":"Fritsche M, Gu S, Timofte R (2019) Frequency separation for real-world super-resolution. In: 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE, p 3599\u20133608","DOI":"10.1109\/ICCVW.2019.00445"},{"key":"1806_CR36","doi-asserted-by":"crossref","unstructured":"Wang L, Wang Y, Dong X, Xu Q, Yang J, An W, Guo Y (2021) Unsupervised degradation representation learning for blind super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, p 10581\u201310590","DOI":"10.1109\/CVPR46437.2021.01044"},{"key":"1806_CR37","doi-asserted-by":"crossref","unstructured":"Zheng H, Ji M, Wang H, Liu Y, Fang L (2018) Crossnet: an end-to-end reference-based super resolution network using cross-scale warping. In: Proceedings of the European conference on computer vision (ECCV), p 88\u2013104","DOI":"10.1007\/978-3-030-01231-1_6"},{"key":"1806_CR38","doi-asserted-by":"crossref","unstructured":"Yang F, Yang H, Fu J, Lu H, Guo B (2020) Learning texture transformer network for image super-resolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, p 5791\u20135800","DOI":"10.1109\/CVPR42600.2020.00583"},{"key":"1806_CR39","doi-asserted-by":"crossref","unstructured":"Lu L, Li W, Tao X, Lu J, Jia J (2021) Masa-sr: matching acceleration and spatial adaptation for reference-based image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, p 6368\u20136377","DOI":"10.1109\/CVPR46437.2021.00630"},{"issue":"3","key":"1806_CR40","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.1364\/BOE.10.001044","volume":"10","author":"H Zhang","year":"2019","unstructured":"Zhang H, Fang C, Xie X, Yang Y, Mei W, Jin D, Fei P (2019) High-throughput, high-resolution deep learning microscopy based on registration-free generative adversarial network. Biomed Opt Express 10(3):1044\u20131063","journal-title":"Biomed Opt Express"},{"key":"1806_CR41","doi-asserted-by":"publisher","first-page":"32795","DOI":"10.1109\/ACCESS.2021.3057497","volume":"9","author":"F Shahidi","year":"2021","unstructured":"Shahidi F (2021) Breast cancer histopathology image super-resolution using wide-attention gan with improved Wasserstein gradient penalty and perceptual loss. IEEE Access 9:32795\u201332809","journal-title":"IEEE Access"},{"key":"1806_CR42","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), p 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"1806_CR43","doi-asserted-by":"crossref","unstructured":"Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) Eca-net: Efficient channel attention for deep convolutional neural networks. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), p 11531\u201311539","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"1806_CR44","doi-asserted-by":"crossref","unstructured":"Wang X, Yu K, Wu S, Gu J, Liu Y, Dong C, Qiao Y, Loy CC (2018) Esrgan: Enhanced super-resolution generative adversarial networks. In: Proceedings of the European conference on computer vision (ECCV) workshops","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"1806_CR45","doi-asserted-by":"crossref","unstructured":"Wang X, Yu K, Dong C, Loy CC (2018) Recovering realistic texture in image super-resolution by deep spatial feature transform. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p 606\u2013615","DOI":"10.1109\/CVPR.2018.00070"},{"key":"1806_CR46","doi-asserted-by":"crossref","unstructured":"Ma C, Rao Y, Cheng Y, Chen C, Lu J, Zhou J (2020) Structure-preserving super resolution with gradient guidance. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, p 7769\u20137778","DOI":"10.1109\/CVPR42600.2020.00779"},{"key":"1806_CR47","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p 1492\u20131500","DOI":"10.1109\/CVPR.2017.634"},{"key":"1806_CR48","doi-asserted-by":"crossref","unstructured":"Isola P, Zhu J-Y, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p 1125\u20131134","DOI":"10.1109\/CVPR.2017.632"},{"key":"1806_CR49","unstructured":"Li Y, Ping W (2018) Cancer metastasis detection with neural conditional random field. arXiv preprint arXiv:1806.07064"},{"key":"1806_CR50","unstructured":"Blau Y, Mechrez R, Timofte R, Michaeli T, Zelnik-Manor L (2018) The pirm challenge on perceptual super resolution"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01806-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-01806-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01806-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,6]],"date-time":"2024-01-06T08:22:00Z","timestamp":1704529320000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-01806-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,3]]},"references-count":50,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["1806"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-01806-9","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,3]]},"assertion":[{"value":"4 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}