{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T15:21:53Z","timestamp":1773328913709,"version":"3.50.1"},"reference-count":78,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s10489-024-05378-1","type":"journal-article","created":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T21:01:35Z","timestamp":1712091695000},"page":"4621-4645","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A two-stage approach solo_GAN for overlapping cervical cell segmentation based on single-cell identification and boundary generation"],"prefix":"10.1007","volume":"54","author":[{"given":"Zihao","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4891-3242","authenticated-orcid":false,"given":"Dongyao","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanwang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziqi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nengkai","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,2]]},"reference":[{"issue":"9590","key":"5378_CR1","doi-asserted-by":"publisher","first-page":"890","DOI":"10.1016\/S0140-6736(07)61416-0","volume":"370","author":"M Schiffman","year":"2007","unstructured":"Schiffman M, Castle PE, Jeronimo J, Rodriguez AC, Wacholder S (2007) Human papillomavirus and cervical cancer. The lancet 370(9590):890\u2013907. https:\/\/doi.org\/10.1016\/S0140-6736(07)61416-0","journal-title":"The lancet"},{"issue":"2","key":"5378_CR2","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.jnma.2020.03.002","volume":"112","author":"A Buskwofie","year":"2020","unstructured":"Buskwofie A, David-West G, Clare CA (2020) A review of cervical cancer: incidence and disparities. J Natl Med Assoc 112(2):229\u2013232. https:\/\/doi.org\/10.1016\/j.jnma.2020.03.002","journal-title":"J Natl Med Assoc"},{"issue":"2","key":"5378_CR3","doi-asserted-by":"publisher","first-page":"e191","DOI":"10.1016\/S2214-109X(19)30482-6","volume":"8","author":"M Arbyn","year":"2020","unstructured":"Arbyn M, Weiderpass E, Bruni L et al (2020) Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis. Lancet Glob Health 8(2):e191\u2013e203. https:\/\/doi.org\/10.1016\/S2214-109X(19)30482-6","journal-title":"Lancet Glob Health"},{"issue":"3","key":"5378_CR4","doi-asserted-by":"publisher","first-page":"575","DOI":"10.3390\/cancers14030575","volume":"14","author":"YM Guimar\u00e3es","year":"2022","unstructured":"Guimar\u00e3es YM, Godoy LR, Longatto-Filho A et al (2022) Management of early-stage cervical cancer: a literature review. Cancers 14(3):575. https:\/\/doi.org\/10.3390\/cancers14030575","journal-title":"Cancers"},{"issue":"3","key":"5378_CR5","doi-asserted-by":"publisher","first-page":"665","DOI":"10.21203\/rs.3.rs-2680912\/v1","volume":"19","author":"P Jiang","year":"2023","unstructured":"Jiang P, Li X, Shen H et al (2023) A Survey on Deep Learning-based Cervical Cytology Screening: from Cell Identification to Whole Slide Image Analysis. Research Square 19(3):665. https:\/\/doi.org\/10.21203\/rs.3.rs-2680912\/v1","journal-title":"Research Square"},{"key":"5378_CR6","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.998222","volume":"12","author":"J Liao","year":"2023","unstructured":"Liao J, Li X, Gan Y et al (2023) Artificial intelligence assists precision medicine in cancer treatment. Front Oncol 12:998222. https:\/\/doi.org\/10.3389\/fonc.2022.998222","journal-title":"Front Oncol"},{"key":"5378_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102197","volume":"73","author":"L Cao","year":"2021","unstructured":"Cao L, Yang J, Rong Z et al (2021) A novel attention-guided convolutional network for the detection of abnormal cervical cells in cervical cancer screening. Med Image Anal 73:102197. https:\/\/doi.org\/10.1016\/j.media.2021.102197","journal-title":"Med Image Anal"},{"key":"5378_CR8","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.851367","volume":"12","author":"X Hou","year":"2022","unstructured":"Hou X, Shen G, Zhou L et al (2022) Artificial intelligence in cervical cancer screening and diagnosis. Front Oncol 12:851367. https:\/\/doi.org\/10.3389\/fonc.2022.851367","journal-title":"Front Oncol"},{"issue":"20","key":"5378_CR9","doi-asserted-by":"publisher","first-page":"5114","DOI":"10.3390\/ijms20205114","volume":"20","author":"T Concei\u00e7\u00e3o","year":"2019","unstructured":"Concei\u00e7\u00e3o T, Braga C, Rosado L et al (2019) A review of computational methods for cervical cells segmentation and abnormality classification. Int J Mol Sci 20(20):5114. https:\/\/doi.org\/10.3390\/ijms20205114","journal-title":"Int J Mol Sci"},{"key":"5378_CR10","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.future.2020.07.045","volume":"114","author":"M Zhao","year":"2021","unstructured":"Zhao M, Wang H, Han Y et al (2021) Seens: Nuclei segmentation in pap smear images with selective edge enhancement. Futur Gener Comput Syst 114:185\u2013194. https:\/\/doi.org\/10.1016\/j.future.2020.07.045","journal-title":"Futur Gener Comput Syst"},{"issue":"5","key":"5378_CR11","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/s11042-020-09206-9","volume":"123","author":"J Ke","year":"2019","unstructured":"Ke J, Jiang Z, Liu C et al (2019) Selective detection and segmentation of cervical cells. Proceedings of the 2019 11th international conference on bioinformatics and biomedical technology 123(5):55\u201361. https:\/\/doi.org\/10.1007\/s11042-020-09206-9","journal-title":"Proceedings of the 2019 11th international conference on bioinformatics and biomedical technology"},{"key":"5378_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113707","volume":"160","author":"J Martinez-Mas","year":"2020","unstructured":"Martinez-Mas J, Bueno-Crespo A, Martinez-Espana R et al (2020) Classifying Papanicolaou cervical smears through a cell merger approach by deep learning technique. Expert Syst Appl 160:113707. https:\/\/doi.org\/10.1016\/j.eswa.2020.113707","journal-title":"Expert Syst Appl"},{"issue":"7","key":"5378_CR13","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1002\/cyto.a.23686","volume":"95","author":"Z Wang","year":"2019","unstructured":"Wang Z et al (2019) Cell segmentation for image cytometry: advances, insufficiencies, and challenges. Cytometry A 95(7):708\u2013711. https:\/\/doi.org\/10.1002\/cyto.a.23686","journal-title":"Cytometry A"},{"issue":"1","key":"5378_CR14","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1504\/WRSTSD.2022.119330","volume":"18","author":"NL Devi","year":"2022","unstructured":"Devi NL, Thirumurugan P et al (2022) A literature survey of automated detection of cervical cancer cell in Pap smear images. World Review of Science, Technology and Sustainable Development 18(1):74\u201382. https:\/\/doi.org\/10.1504\/WRSTSD.2022.119330","journal-title":"World Review of Science, Technology and Sustainable Development"},{"issue":"4","key":"5378_CR15","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1007673","volume":"16","author":"JB Lugagne","year":"2020","unstructured":"Lugagne JB, Lin H, Dunlop MJ (2020) DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning. PLoS Comput Biol 16(4):e1007673","journal-title":"PLoS Comput Biol"},{"issue":"11","key":"5378_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102691","volume":"35","author":"H Jiang","year":"2022","unstructured":"Jiang H, Zhou Y, Lin Y et al (2022) Deep learning for computational cytology: A survey. Med Image Anal 35(11):102691. https:\/\/doi.org\/10.1016\/j.media.2022.102691","journal-title":"Med Image Anal"},{"issue":"1","key":"5378_CR17","doi-asserted-by":"publisher","first-page":"47","DOI":"10.3390\/bioengineering10010047","volume":"10","author":"Y Zhao","year":"2022","unstructured":"Zhao Y, Fu C, Zhang W et al (2022) Automatic Segmentation of Cervical Cells Based on Star-Convex Polygons in Pap Smear Images. Bioengineering 10(1):47. https:\/\/doi.org\/10.3390\/bioengineering10010047","journal-title":"Bioengineering"},{"issue":"9","key":"5378_CR18","doi-asserted-by":"publisher","first-page":"2432","DOI":"10.1109\/TMI.2022.3163171","volume":"41","author":"T Chen","year":"2022","unstructured":"Chen T, Zheng W, Ying H et al (2022) A task decomposing and cell comparing method for cervical lesion cell detection. IEEE Trans Med Imaging 41(9):2432\u20132442. https:\/\/doi.org\/10.1109\/TMI.2022.3163171","journal-title":"IEEE Trans Med Imaging"},{"key":"5378_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2021.100723","volume":"26","author":"A Anaya-Isaza","year":"2021","unstructured":"Anaya-Isaza A, Mera-Jim\u00e9nez L, Zequera-Diaz M (2021) An overview of deep learning in medical imaging. Informatics in medicine unlocked 26:100723","journal-title":"Informatics in medicine unlocked"},{"issue":"10","key":"5378_CR20","doi-asserted-by":"publisher","first-page":"2421","DOI":"10.1109\/RBME.2021.3136343","volume":"45","author":"S Bohlender","year":"2021","unstructured":"Bohlender S, Oksuz I, Mukhopadhyay A (2021) A survey on shape-constraint deep learning for medical image segmentation. IEEE Rev Biomed Eng 45(10):2421\u20132564. https:\/\/doi.org\/10.1109\/RBME.2021.3136343","journal-title":"IEEE Rev Biomed Eng"},{"issue":"5","key":"5378_CR21","doi-asserted-by":"publisher","first-page":"1800","DOI":"10.3390\/app10051800","volume":"10","author":"KP Win","year":"2020","unstructured":"Win KP, Kitjaidure Y, Hamamoto K et al (2020) Computer-assisted screening for cervical cancer using digital image processing of pap smear images. Appl Sci 10(5):1800. https:\/\/doi.org\/10.3390\/app10051800","journal-title":"Appl Sci"},{"key":"5378_CR22","doi-asserted-by":"publisher","first-page":"24157","DOI":"10.1007\/s11042-020-09206-9","volume":"79","author":"M Arya","year":"2020","unstructured":"Arya M, Mittal N, Singh G (2020) Three segmentation techniques to predict the dysplasia in cervical cells in the presence of debris. Multimedia Tools and Applications 79:24157\u201324172. https:\/\/doi.org\/10.1007\/s11042-020-09206-9","journal-title":"Multimedia Tools and Applications"},{"key":"5378_CR23","doi-asserted-by":"publisher","first-page":"2341","DOI":"10.1007\/s10462-019-09735-2","volume":"53","author":"A Sarwar","year":"2020","unstructured":"Sarwar A, Sheikh AA, Manhas J et al (2020) Segmentation of cervical cells for automated screening of cervical cancer: a review. Artif Intell Rev 53:2341\u20132379. https:\/\/doi.org\/10.1007\/s10462-019-09735-2","journal-title":"Artif Intell Rev"},{"issue":"12","key":"5378_CR24","doi-asserted-by":"publisher","first-page":"2849","DOI":"10.1109\/TMI.2019.2915633","volume":"38","author":"Y Song","year":"2019","unstructured":"Song Y, Zhu L, Qin J et al (2019) Segmentation of overlapping cytoplasm in cervical smear images via adaptive shape priors extracted from contour fragment. IEEE Trans Med Imaging 38(12):2849\u20132862. https:\/\/doi.org\/10.1109\/TMI.2019.2915633","journal-title":"IEEE Trans Med Imaging"},{"issue":"9","key":"5378_CR25","doi-asserted-by":"publisher","first-page":"2044","DOI":"10.1109\/TMI.2018.2815013","volume":"37","author":"A Tareef","year":"2018","unstructured":"Tareef A, Song Y, Huang H et al (2018) Multi-pass fast watershed for accurate segmentation of overlapping cervical cells. IEEE Trans Med Imaging 37(9):2044\u20132059. https:\/\/doi.org\/10.1109\/TMI.2018.2815013","journal-title":"IEEE Trans Med Imaging"},{"key":"5378_CR26","doi-asserted-by":"publisher","first-page":"2399","DOI":"10.1109\/ICPR.2010.587","volume-title":"2010 20th international conference on pattern recognition","author":"A Kale","year":"2010","unstructured":"Kale A, Aksoy S (2010) Segmentation of cervical cell images. In: 2010 20th international conference on pattern recognition. IEEE 45(6):239\u20132402"},{"key":"5378_CR27","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.neucom.2019.06.086","volume":"365","author":"T Wan","year":"2019","unstructured":"Wan T, Xu S, Sang C et al (2019) Accurate segmentation of overlapping cells in cervical cytology with deep convolutional neural networks. Neurocomputing 365:157\u2013170. https:\/\/doi.org\/10.1016\/j.neucom.2019.06.086","journal-title":"Neurocomputing"},{"key":"5378_CR28","doi-asserted-by":"publisher","first-page":"4821","DOI":"10.1007\/s10462-020-09808-7","volume":"53","author":"C Li","year":"2020","unstructured":"Li C, Chen H, Li X et al (2020) A review for cervical histopathology image analysis using machine vision approaches. Artif Intell Rev 53:4821\u20134862. https:\/\/doi.org\/10.1007\/s10462-020-09808-7","journal-title":"Artif Intell Rev"},{"key":"5378_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112951","volume":"141","author":"Z Alyafeai","year":"2020","unstructured":"Alyafeai Z, Ghouti L et al (2020) A fully-automated deep learning pipeline for cervical cancer classification. Expert Syst Appl 141:112951. https:\/\/doi.org\/10.1016\/j.eswa.2019.112951","journal-title":"Expert Syst Appl"},{"issue":"1","key":"5378_CR30","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1109\/TMI.2019.2913056","volume":"36","author":"Y Song","year":"2016","unstructured":"Song Y, Tan EL, Jiang X et al (2016) Accurate cervical cell segmentation from overlapping clumps in pap smear images. IEEE Trans Med Imaging 36(1):288\u2013300. https:\/\/doi.org\/10.1109\/TMI.2019.2913056","journal-title":"IEEE Trans Med Imaging"},{"key":"5378_CR31","doi-asserted-by":"publisher","first-page":"116925","DOI":"10.1109\/ACCESS.2019.2936017","volume":"7","author":"KHS Allehaibi","year":"2019","unstructured":"Allehaibi KHS, Nugroho LE, Lazuardi L et al (2019) Segmentation and classification of cervical cells using deep learning. IEEE Access 7:116925\u2013116941. https:\/\/doi.org\/10.1109\/ACCESS.2019.2936017","journal-title":"IEEE Access"},{"key":"5378_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105500","volume":"145","author":"Y Zhao","year":"2022","unstructured":"Zhao Y, Fu C, Xu S et al (2022) LFANet: Lightweight feature attention network for abnormal cell segmentation in cervical cytology images. Comput Biol Med 145:105500. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105500","journal-title":"Comput Biol Med"},{"issue":"1","key":"5378_CR33","doi-asserted-by":"publisher","first-page":"16244","DOI":"10.1038\/s41598-021-95545-y","volume":"11","author":"CW Wang","year":"2021","unstructured":"Wang CW, Liou YA, Lin YJ et al (2021) Artificial intelligence-assisted fast screening cervical high grade squamous intraepithelial lesion and squamous cell carcinoma diagnosis and treatment planning. Sci Rep 11(1):16244. https:\/\/doi.org\/10.1038\/s41598-021-95545-y","journal-title":"Sci Rep"},{"key":"5378_CR34","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer International Publishing 12(8):234\u2013241. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"5378_CR35","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/ICPR48806.2021.9413098","volume-title":"2020 25th international conference on pattern recognition (ICPR)","author":"A Khadangi","year":"2021","unstructured":"Khadangi A, Boudier T, Rajagopal V. (2021) EM-net: Deep learning for electron microscopy image segmentation. In: 2020 25th international conference on pattern recognition (ICPR). IEEE 45(2):31\u201338. https:\/\/doi.org\/10.1109\/ICPR48806.2021.9413098"},{"issue":"4","key":"5378_CR36","doi-asserted-by":"publisher","first-page":"271","DOI":"10.14257\/ijca.2017.10.4.24","volume":"10","author":"G Ting","year":"2017","unstructured":"Ting G, Weixing W, Wei L et al (2017) Rock particle image segmentation based on improved normalized cut. International Journal of Control and Automation 10(4):271\u2013286","journal-title":"International Journal of Control and Automation"},{"key":"5378_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.105063","volume":"140","author":"S Xun","year":"2022","unstructured":"Xun S, Li D, Zhu H et al (2022) Generative adversarial networks in medical image segmentation: A review. Comput Biol Med 140:105063. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.105063","journal-title":"Comput Biol Med"},{"issue":"3","key":"5378_CR38","first-page":"4","volume":"2","author":"D Pathak","year":"2016","unstructured":"Pathak D, Krahenbuhl P, Donahue J et al (2016) Context encoders: Feature learning by inpainting. InProceedings of the IEE CVF Conference on Computer Vision and Pattern Recognition 2(3):4","journal-title":"InProceedings of the IEE CVF Conference on Computer Vision and Pattern Recognition"},{"issue":"1","key":"5378_CR39","first-page":"2223","volume":"98","author":"JY Zhu","year":"2017","unstructured":"Zhu JY, Park T, Isola P et al (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. Proc IEEE Int Conf Comput Vis 98(1):2223\u20132232","journal-title":"Proc IEEE Int Conf Comput Vis"},{"issue":"13","key":"5378_CR40","first-page":"4681","volume":"11","author":"C Ledig","year":"2017","unstructured":"Ledig C, Theis L, Husz\u00e1r F et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. Proc IEEE Conf Comput Vis Pattern Recognit 11(13):4681\u20134690","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"5378_CR41","first-page":"1060","volume-title":"International conference on machine learning","author":"S Reed","year":"2016","unstructured":"Reed S, Akata Z, Yan X et al (2016) Generative adversarial text to image synthesis. In: International conference on machine learning, vol 29. PMLR, pp 1060\u20131069"},{"key":"5378_CR42","doi-asserted-by":"publisher","first-page":"115415","DOI":"10.1109\/ACCESS.2021.3104609","volume":"9","author":"J Huang","year":"2021","unstructured":"Huang J, Yang G, Li B et al (2021) Segmentation of cervical cell images based on generative adversarial networks. IEEE Access 9:115415\u2013115428. https:\/\/doi.org\/10.1109\/ACCESS.2021.3104609","journal-title":"IEEE Access"},{"issue":"12","key":"5378_CR43","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1038\/s41568-021-00399-1","volume":"21","author":"O Elemento","year":"2021","unstructured":"Elemento O, Leslie C, Lundin J et al (2021) Artificial intelligence in cancer research, diagnosis and therapy. Nat Rev Cancer 21(12):747\u2013752. https:\/\/doi.org\/10.1038\/s41568-021-00399-1","journal-title":"Nat Rev Cancer"},{"key":"5378_CR44","doi-asserted-by":"publisher","first-page":"2903","DOI":"10.1109\/EMBC.2014.6944230","volume-title":"2014 36th annual international conference of the IEEE engineering in medicine and biology society","author":"Y Song","year":"2014","unstructured":"Song Y, Zhang L, Chen S et al (2014) A deep learning based framework for accurate segmentation of cervical cytoplasm and nuclei. In: 2014 36th annual international conference of the IEEE engineering in medicine and biology society. IEEE 66(12):2903\u20132906. https:\/\/doi.org\/10.1109\/EMBC.2014.6944230"},{"key":"5378_CR45","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.compmedimag.2019.01.003","volume":"72","author":"FHD Ara\u00fajo","year":"2019","unstructured":"Ara\u00fajo FHD, Silva RRV, Ushizima DM et al (2019) Deep learning for cell image segmentation and ranking. Comput Med Imaging Graph 72:13\u201321. https:\/\/doi.org\/10.1016\/j.compmedimag.2019.01.003","journal-title":"Comput Med Imaging Graph"},{"key":"5378_CR46","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1117\/12.2293926","volume":"10574","author":"L Chen","year":"2018","unstructured":"Chen L, Shen C, Li S et al (2018) Automatic PET cervical tumor segmentation by deep learning with prior information. Medical Imaging 2018: Image Processing. SPIE 10574:834\u2013839. https:\/\/doi.org\/10.1117\/12.2293926","journal-title":"SPIE"},{"key":"5378_CR47","doi-asserted-by":"publisher","first-page":"32559","DOI":"10.1109\/ACCESS.2021.3060447","volume":"9","author":"S Yu","year":"2021","unstructured":"Yu S, Feng X, Wang B, Dun H, Zhang S, Zhang R, Huang X (2021) Automatic classification of cervical cells using deep learning method. IEEE Access 9:32559\u201332568","journal-title":"IEEE Access"},{"issue":"7","key":"5378_CR48","doi-asserted-by":"publisher","first-page":"4066","DOI":"10.1080\/03772063.2021.1958075","volume":"69","author":"TS SheelaShiney","year":"2023","unstructured":"SheelaShiney TS, Rose RJ (2023) Deep auto encoder based extreme learning system for automatic segmentation of cervical cells. IETE J Res 69(7):4066\u20134086. https:\/\/doi.org\/10.1080\/03772063.2021.1958075","journal-title":"IETE J Res"},{"issue":"1","key":"5378_CR49","doi-asserted-by":"publisher","first-page":"5639","DOI":"10.1038\/s41467-021-25296-x","volume":"12","author":"S Cheng","year":"2021","unstructured":"Cheng S, Liu S, Yu J et al (2021) Robust whole slide image analysis for cervical cancer screening using deep learning. Nat Commun 12(1):5639","journal-title":"Nat Commun"},{"key":"5378_CR50","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.media.2018.08.005","volume":"50","author":"Z Han","year":"2018","unstructured":"Han Z, Wei B, Mercado A et al (2018) Spine-GAN: Semantic segmentation of multiple spinal structures. Med Image Anal 50:23\u201335. https:\/\/doi.org\/10.1016\/j.media.2018.08.005","journal-title":"Med Image Anal"},{"key":"5378_CR51","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1117\/12.2254487","volume":"10133","author":"VMS AlexKP","year":"2017","unstructured":"AlexKP VMS, Chennamsetty SS et al (2017) Generative adversarial networks for brain lesion detection. Medical Imaging 2017: Image Processing. SPIE 10133:113\u2013121. https:\/\/doi.org\/10.1117\/12.2254487","journal-title":"SPIE"},{"issue":"6","key":"5378_CR52","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1109\/TMI.2018.2820120","volume":"37","author":"TM Quan","year":"2018","unstructured":"Quan TM, Nguyen-Duc T, Jeong WK (2018) Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss. IEEE Trans Med Imaging 37(6):1488\u20131497. https:\/\/doi.org\/10.1109\/TMI.2018.2820120","journal-title":"IEEE Trans Med Imaging"},{"issue":"12","key":"5378_CR53","doi-asserted-by":"publisher","first-page":"2572","DOI":"10.1109\/TMI.2018.2842767","volume":"37","author":"F Mahmood","year":"2018","unstructured":"Mahmood F, Chen R, Durr NJ (2018) Unsupervised reverse domain adaptation for synthetic medical images via adversarial training. IEEE Trans Med Imaging 37(12):2572\u20132581. https:\/\/doi.org\/10.1109\/TMI.2018.2842767","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"5378_CR54","doi-asserted-by":"publisher","first-page":"16884","DOI":"10.1038\/s41598-019-52737-x","volume":"9","author":"V Sandfort","year":"2019","unstructured":"Sandfort V, Yan K, Pickhardt PJ et al (2019) Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks. Sci Rep 9(1):16884","journal-title":"Sci Rep"},{"issue":"4","key":"5378_CR55","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.1109\/JBHI.2021.3094311","volume":"26","author":"R Elakkiya","year":"2021","unstructured":"Elakkiya R, Subramaniyaswamy V, Vijayakumar V et al (2021) Cervical cancer diagnostics healthcare system using hybrid object detection adversarial networks. IEEE J Biomed Health Inform 26(4):1464\u20131471. https:\/\/doi.org\/10.1109\/JBHI.2021.3094311","journal-title":"IEEE J Biomed Health Inform"},{"key":"5378_CR56","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1109\/ICAIBD49809.2020.9137494","volume-title":"2020 3rd international conference on artificial intelligence and big data (ICAIBD)","author":"S Chen","year":"2020","unstructured":"Chen S, Gao D, Wang L et al (2020) Cervical cancer single cell image data augmentation using residual condition generative adversarial networks. In: 2020 3rd international conference on artificial intelligence and big data (ICAIBD). IEEE 23(12):237\u2013241. https:\/\/doi.org\/10.1109\/ICAIBD49809.2020.9137494"},{"key":"5378_CR57","doi-asserted-by":"publisher","first-page":"4487","DOI":"10.1109\/EMBC.2019.8857124","volume-title":"The 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC)","author":"P Ganesan","year":"2019","unstructured":"Ganesan P, Xue Z, Singh S et al (2019) Performance evaluation of a generative adversarial network for deblurring mobile-phone cervical images. In: The 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE 41(3):4487\u20134490. https:\/\/doi.org\/10.1109\/EMBC.2019.8857124"},{"key":"5378_CR58","first-page":"7354","volume-title":"International conference on machine learning","author":"H Zhang","year":"2019","unstructured":"Zhang H, Goodfellow I, Metaxas D et al (2019) Self-attention generative adversarial networks. In: International conference on machine learning. PMLR 24(12):7354\u20137363. https:\/\/proceedings.mlr.press\/v97\/zhang19d.html"},{"key":"5378_CR59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-030-59354-4_1","volume-title":"International workshop on predictive intelligence in medicine","author":"N Bnouni","year":"2020","unstructured":"Bnouni N, Rekik I, Rhim MS et al (2020) Context-aware synergetic multiplex network for multi-organ segmentation of cervical cancer MRI. International workshop on predictive intelligence in medicine. Cham: Springer International Publishing 22(12):1\u201311. https:\/\/doi.org\/10.1007\/978-3-030-59354-4_1"},{"key":"5378_CR60","first-page":"195","volume-title":"International conference on machine learning","author":"A Almahairi","year":"2018","unstructured":"Almahairi A, Rajeshwar S, Sordoni A et al (2018) Augmented cyclegan: Learning many-to-many mappings from unpaired data. In: International conference on machine learning. PMLR 6(7):195\u2013204. https:\/\/proceedings.mlr.press\/v80\/almahairi18a.html"},{"issue":"6","key":"5378_CR61","doi-asserted-by":"publisher","first-page":"8939","DOI":"10.1007\/s11042-022-11954-9","volume":"81","author":"D Jia","year":"2022","unstructured":"Jia D, He Z, Zhang C et al (2022) Detection of cervical cancer cells in complex situation based on improved YOLOv3 network. Multimedia Tools and Applications 81(6):8939\u20138961. https:\/\/doi.org\/10.1007\/s11042-022-11954-9","journal-title":"Multimedia Tools and Applications"},{"key":"5378_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-019-3332","volume":"21","author":"F Long","year":"2020","unstructured":"Long F (2020) Microscopy cell nuclei segmentation with enhanced U-Net. BMC Bioinformatics 21:1\u201312. https:\/\/doi.org\/10.1186\/s12859-019-3332","journal-title":"BMC Bioinformatics"},{"issue":"68","key":"5378_CR63","first-page":"120","volume":"12","author":"O Commowick","year":"2021","unstructured":"Commowick O, Cervenansky F, Cotton F et al (2021) MSSEG-2 challenge proceedings: Multiple sclerosis new lesions segmentation challenge using a data management and processing infrastructure. MICCAI 2021-24th international conference on medical image computing and computer assisted intervention 12(68):120\u2013126. https:\/\/inria.hal.science\/hal-03358968v3","journal-title":"MICCAI 2021-24th international conference on medical image computing and computer assisted intervention"},{"key":"5378_CR64","doi-asserted-by":"publisher","first-page":"18450","DOI":"10.1109\/ACCESS.2019.2896409","volume":"7","author":"Q Wang","year":"2019","unstructured":"Wang Q, Zhou X, Wang C et al (2019) WGAN-based synthetic minority over-sampling technique: improving semantic fine-grained classification for lung nodules in CT images. IEEE Access 7:18450\u201318463. https:\/\/doi.org\/10.1109\/ACCESS.2019.2896409","journal-title":"IEEE Access"},{"issue":"10","key":"5378_CR65","doi-asserted-by":"publisher","first-page":"13371","DOI":"10.1007\/s11042-021-11015-7","volume":"81","author":"D Jia","year":"2022","unstructured":"Jia D, Zhou J, Zhang C (2022) Detection of cervical cells based on improved SSD network. Multimedia Tools and Applications 81(10):13371\u201313387. https:\/\/doi.org\/10.1007\/s11042-021-11015-7","journal-title":"Multimedia Tools and Applications"},{"issue":"46","key":"5378_CR66","doi-asserted-by":"publisher","first-page":"1996","DOI":"10.1109\/ICSP54964.2022.9778577","volume":"78","author":"C Shi","year":"2022","unstructured":"Shi C, Pan Q, Rehman M (2022) Cervical cancer cell image detection method based on improved YOLOv4. IEEE 2022 7th international conference on intelligent computing and signal processing (ICSP) 78(46):1996\u20132000. https:\/\/doi.org\/10.1109\/ICSP54964.2022.9778577","journal-title":"IEEE 2022 7th international conference on intelligent computing and signal processing (ICSP)"},{"issue":"2","key":"5378_CR67","doi-asserted-by":"publisher","first-page":"2364","DOI":"10.3934\/mbe.2023111","volume":"20","author":"N Wu","year":"2023","unstructured":"Wu N, Jia D, Zhang C et al (2023) Cervical cell extraction network based on optimized yolo. Math Biosci Eng 20(2):2364\u20132381. https:\/\/doi.org\/10.3934\/mbe.2023111","journal-title":"Math Biosci Eng"},{"issue":"12","key":"5378_CR68","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICASET.2019.8714263","volume":"45","author":"H Kaldera","year":"2019","unstructured":"Kaldera H, Gunasekara SR, Dissanayake MB (2019) Brain tumor classification and segmentation using faster R-CNN. IEEE 2019 advances in science and engineering technology international conferences (ASET) 45(12):1\u20136. https:\/\/doi.org\/10.1109\/ICASET.2019.8714263","journal-title":"IEEE 2019 advances in science and engineering technology international conferences (ASET)"},{"key":"5378_CR69","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103589","volume":"75","author":"J Chen","year":"2022","unstructured":"Chen J, Li P, Xu T et al (2022) Detection of cervical lesions in colposcopic images based on the RetinaNet method. Biomed Signal Process Control 75:103589. https:\/\/doi.org\/10.1016\/j.bspc.2022.103589","journal-title":"Biomed Signal Process Control"},{"issue":"16","key":"5378_CR70","doi-asserted-by":"publisher","first-page":"3738","DOI":"10.1109\/CVPRW53098.2021.00414","volume":"75","author":"Z Meng","year":"2021","unstructured":"Meng Z, Zhao Z, Su F et al (2021) Hierarchical spatial pyramid network for cervical precancerous segmentation by reconstructing deep segmentation networks. Proc IEEE\/CVF Conf Comput Vis Pattern Recognit 75(16):3738\u20133745. https:\/\/doi.org\/10.1109\/CVPRW53098.2021.00414","journal-title":"Proc IEEE\/CVF Conf Comput Vis Pattern Recognit"},{"key":"5378_CR71","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.neucom.2020.06.006","volume":"411","author":"AD Jia","year":"2020","unstructured":"Jia AD, Li BZ, Zhang CC (2020) Detection of cervical cancer cells based on strong feature CNN-SVM network. Neurocomputing 411:112\u2013127. https:\/\/doi.org\/10.1016\/j.neucom.2020.06.006","journal-title":"Neurocomputing"},{"issue":"5","key":"5378_CR72","doi-asserted-by":"publisher","first-page":"3245","DOI":"10.1155\/2016\/9535027","volume":"30","author":"J Su","year":"2016","unstructured":"Su J, Xu X, He Y et al (2016) (2016) Automatic detection of cervical cancer cells by a two-level cascade classification system. Anal Cell Pathol 30(5):3245\u20133605. https:\/\/doi.org\/10.1155\/2016\/9535027","journal-title":"Anal Cell Pathol"},{"issue":"5","key":"5378_CR73","doi-asserted-by":"publisher","first-page":"3585","DOI":"10.3390\/curroncol28050307","volume":"28","author":"X Li","year":"2021","unstructured":"Li X, Xu Z, Shen X et al (2021) Detection of cervical cancer cells in whole slide images using deformable and global context aware faster RCNN-FPN. Curr Oncol 28(5):3585\u20133601. https:\/\/doi.org\/10.3390\/curroncol28050307","journal-title":"Curr Oncol"},{"key":"5378_CR74","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101897","volume":"107","author":"E Hussain","year":"2020","unstructured":"Hussain E, Mahanta LB, Das CR et al (2020) A shape context fully convolutional neural network for segmentation and classification of cervical nuclei in Pap smear images. Artif Intell Med 107:101897. https:\/\/doi.org\/10.1016\/j.artmed.2020.101897","journal-title":"Artif Intell Med"},{"key":"5378_CR75","doi-asserted-by":"publisher","first-page":"52106","DOI":"10.1155\/2021\/3890988","volume":"105","author":"J Chen","year":"2021","unstructured":"Chen J, Zhang B (2021) Segmentation of overlapping cervical cells with mask region convolutional neural network. Comput Math Methods Med 105:52106. https:\/\/doi.org\/10.1155\/2021\/3890988","journal-title":"Comput Math Methods Med"},{"key":"5378_CR76","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2021.103248","volume":"210","author":"R El Jurdi","year":"2021","unstructured":"El Jurdi R, Petitjean C, Honeine P et al (2021) High-level prior-based loss functions for medical image segmentation: A survey. Comput Vis Image Underst 210:103248. https:\/\/doi.org\/10.1016\/j.cviu.2021.103248","journal-title":"Comput Vis Image Underst"},{"key":"5378_CR77","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104902","volume":"85","author":"J Chaki","year":"2023","unstructured":"Chaki J, Wo\u017aniak M (2023) A deep learning based four-fold approach to classify brain MRI: BTSCNet. Biomed Signal Process Control 85:104902. https:\/\/doi.org\/10.1016\/j.bspc.2023.104902","journal-title":"Biomed Signal Process Control"},{"key":"5378_CR78","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104223","volume":"80","author":"J Chaki","year":"2023","unstructured":"Chaki J, Wo\u017aniak M (2023) Deep learning for neurodegenerative disorder (2016 to 2022): A systematic review. Biomed Signal Process Control 80:104223. https:\/\/doi.org\/10.1016\/j.bspc.2022.104223","journal-title":"Biomed Signal Process Control"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05378-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05378-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05378-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T13:46:12Z","timestamp":1714398372000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05378-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3]]},"references-count":78,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["5378"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05378-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3]]},"assertion":[{"value":"7 March 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This study was conducted in accordance with the ethical guidelines and regulations set forth by Guangdong Provincial People\u2019s Hospital Ethics Committee. Prior to data collection, written informed consent was obtained from all participants involved in the study.The data used in this study were de-identified and stored securely to maintain confidentiality. Only authorized researchers had access to the data, and all data handling and analysis procedures complied with applicable privacy laws and regulations.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical considerations and informed consent"}}]}}