{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T16:14:03Z","timestamp":1747152843459,"version":"3.40.5"},"publisher-location":"Cham","reference-count":102,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031903977"},{"type":"electronic","value":"9783031903984"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/16833_2024_217","type":"book-chapter","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T16:04:00Z","timestamp":1711641840000},"page":"309-334","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Precise Identification of Different Cervical Intraepithelial Neoplasia (CIN) Stages, Using Biomedical Engineering Combined with Data Mining and Machine Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0020-8958","authenticated-orcid":false,"given":"Michal","family":"Kruczkowski","sequence":"first","affiliation":[]},{"given":"Anna","family":"Drabik-Kruczkowska","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2673-8313","authenticated-orcid":false,"given":"Roland","family":"Weso\u0142owski","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9900-3951","authenticated-orcid":false,"given":"Anna","family":"Kloska","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6042-701X","authenticated-orcid":false,"given":"Maria Rosario","family":"Pinheiro","sequence":"additional","affiliation":[]},{"given":"Lu\u00eds","family":"Fernandes","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3300-5794","authenticated-orcid":false,"given":"Sebastian Garcia","family":"Galan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,29]]},"reference":[{"issue":"6","key":"217_CR1","doi-asserted-by":"publisher","first-page":"529","DOI":"10.7785\/tcrtexpress.2013.600273","volume":"13","author":"UR Acharya","year":"2014","unstructured":"Acharya UR, Sree SV, Kulshreshtha S, Molinari F, En Wei Koh J, Saba L et al (2014) GyneScan: an improved online paradigm for screening of ovarian cancer via tissue characterization. Technol Cancer Res Treat 13(6):529\u2013539. https:\/\/doi.org\/10.7785\/tcrtexpress.2013.600273","journal-title":"Technol Cancer Res Treat"},{"key":"217_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102164","volume":"120","author":"M Akazawa","year":"2021","unstructured":"Akazawa M, Hashimoto K (2021) Artificial intelligence in gynecologic cancers: current status and future challenges \u2013 a systematic review. Artif Intell Med 120:102164. https:\/\/doi.org\/10.1016\/j.artmed.2021.102164. Epub 2021 Sep 3. PMID: 34629152","journal-title":"Artif Intell Med"},{"key":"217_CR3","doi-asserted-by":"publisher","unstructured":"Albuquerque T, Cardoso JS (2021) Embedded regularization for classification of colposcopic images. In Proceedings \u2013 international symposium on biomedical imaging, IEEE Computer Society, 1920\u20131923. https:\/\/doi.org\/10.1109\/ISBI48211.2021.9433871","DOI":"10.1109\/ISBI48211.2021.9433871"},{"key":"217_CR4","doi-asserted-by":"publisher","unstructured":"Allahqoli L et al (2022) Diagnosis of cervical cancer and pre-cancerous lesions by artificial intelligence: a systematic review. Diagnostics 12(11). https:\/\/doi.org\/10.3390\/diagnostics12112771","DOI":"10.3390\/diagnostics12112771"},{"key":"217_CR5","doi-asserted-by":"publisher","first-page":"2756","DOI":"10.3390\/diagnostics12112756","volume":"12","author":"M Alsalatie","year":"2022","unstructured":"Alsalatie M, Alquran H, Mustafa WA, Yacob YM, Alayed AA (2022) Analysis of cytology pap smear images based on ensemble deep learning approach. Diagnostics 12:2756. https:\/\/doi.org\/10.3390\/diagnostics12112756","journal-title":"Diagnostics"},{"issue":"3","key":"217_CR6","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1016\/j.ultrasmedbio.2015.11.014","volume":"42","author":"V Aramend\u00eda-Vidaurreta","year":"2016","unstructured":"Aramend\u00eda-Vidaurreta V, Cabeza R, Villanueva A, Navallas J, Alcazar JL (2016) Ultrasound image discrimination between benign and malignant adnexal masses based on a neural network approach. Ultrasound Med Biol 42(3):742\u2013752. https:\/\/doi.org\/10.1016\/j.ultrasmedbio.2015.11.014","journal-title":"Ultrasound Med Biol"},{"issue":"6","key":"217_CR7","doi-asserted-by":"publisher","DOI":"10.2196\/15154","volume":"22","author":"O Asan","year":"2020","unstructured":"Asan O, Bayrak AE, Choudhury A (2020) Artificial intelligence and human trust in healthcare: focus on clinicians. J Med Internet Res 22(6):e15154. https:\/\/doi.org\/10.2196\/15154","journal-title":"J Med Internet Res"},{"key":"217_CR8","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39:2481\u20132495. https:\/\/doi.org\/10.1109\/TPAMI.2016.2644615","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"217_CR9","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.ygyno.2020.07.099","volume":"159","author":"H Bao","year":"2020","unstructured":"Bao H et al (2020) Artificial intelligence-assisted cytology for detection of cervical intraepithelial neoplasia or invasive cancer: a multicenter, clinical-based, observational study. Gynecol Oncol 159:171\u2013178. https:\/\/doi.org\/10.1016\/j.ygyno.2020.07.099","journal-title":"Gynecol Oncol"},{"key":"217_CR10","doi-asserted-by":"publisher","first-page":"671","DOI":"10.2139\/ssrn.2477899","volume":"104","author":"S Barocas","year":"2016","unstructured":"Barocas S, Selbst AD (2016) Big data\u2019s disparate impact. Calif Law Rev 104:671. https:\/\/doi.org\/10.2139\/ssrn.2477899","journal-title":"Calif Law Rev"},{"key":"217_CR11","doi-asserted-by":"publisher","first-page":"899","DOI":"10.14569\/IJACSA.2022.01309104","volume":"13","author":"KP Battula","year":"2022","unstructured":"Battula KP, Chandana BS (2022) Deep learning based cervical cancer classification and segmentation from pap smears images using an EfficientNet. Int J Adv Comput Sci Appl 13:899\u2013908. https:\/\/doi.org\/10.14569\/IJACSA.2022.01309104","journal-title":"Int J Adv Comput Sci Appl"},{"key":"217_CR12","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.media.2017.04.008","volume":"39","author":"A BenTaieb","year":"2017","unstructured":"BenTaieb A, Li-Chang H, Huntsman D, Hamarneh G (2017) A structured latent model for ovarian carcinoma subtyping from histopathology slides. Med Image Anal 39:194\u2013205. https:\/\/doi.org\/10.1016\/j.media.2017.04.008","journal-title":"Med Image Anal"},{"issue":"6","key":"217_CR13","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1586\/14737159.5.6.857","volume":"5","author":"JS Bentz","year":"2005","unstructured":"Bentz JS (2005) Liquid-based cytology for cervical cancer screening. Expert Rev Mol Diagn 5(6):857\u2013871. https:\/\/doi.org\/10.1586\/14737159.5.6.857","journal-title":"Expert Rev Mol Diagn"},{"key":"217_CR14","doi-asserted-by":"publisher","first-page":"307","DOI":"10.14445\/22315381\/IJETT-V70I10P230","volume":"70","author":"S Bhattacharjee","year":"2022","unstructured":"Bhattacharjee S, Ray D, Saha D, Sobya D (2022) Classifying pap smear images with an advanced composite random forest model. Int J Eng Trends Technol 70:307\u2013318. https:\/\/doi.org\/10.14445\/22315381\/IJETT-V70I10P230","journal-title":"Int J Eng Trends Technol"},{"key":"217_CR15","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1016\/j.ebiom.2019.10.053","volume":"50","author":"S Bowden","year":"2019","unstructured":"Bowden S, Kalliala I, Veroniki AA, Arbyn M, Mitra A, Lathouras K, Mirabello L, Chadeau-Hyam M, Paraskevaidis E, Flanagan JM, Kyrgiou M (2019) The use of human papillomavirus DNA methylation in cervical intraepithelial neoplasia: a systematic review and meta-analysis. EBioMedicine 50:246\u2013259. https:\/\/doi.org\/10.1016\/j.ebiom.2019.10.053","journal-title":"EBioMedicine"},{"key":"217_CR16","doi-asserted-by":"publisher","unstructured":"Buiu C, D\u0103n\u0103il\u0103 VR, R\u0103du\u0163\u0103 CN (2020) MobileNetV2 ensemble for cervical precancerous lesions classification. PRO 8(5). https:\/\/doi.org\/10.3390\/PR8050595","DOI":"10.3390\/PR8050595"},{"key":"217_CR17","doi-asserted-by":"publisher","first-page":"5584004","DOI":"10.1155\/2021\/5584004","volume":"2021","author":"V Chandran","year":"2021","unstructured":"Chandran V et al (2021) Diagnosis of cervical cancer based on ensemble deep learning network using colposcopy images. Biomed Res Int 2021:5584004. https:\/\/doi.org\/10.1155\/2021\/5584004","journal-title":"Biomed Res Int"},{"issue":"12","key":"217_CR18","doi-asserted-by":"publisher","first-page":"3066","DOI":"10.3390\/diagnostics12123066","volume":"12","author":"M Chen","year":"2022","unstructured":"Chen M et al (2022) Evaluating the feasibility of machine-learning-based predictive models for precancerous cervical lesions in patients referred for colposcopy. Diagnostics (Basel) 12(12):3066. https:\/\/doi.org\/10.3390\/diagnostics12123066. PMID: 36553073; PMCID: PMC9776471","journal-title":"Diagnostics (Basel)"},{"issue":"7","key":"217_CR19","doi-asserted-by":"publisher","first-page":"8690","DOI":"10.1002\/cam4.5581","volume":"12","author":"X Chen","year":"2023","unstructured":"Chen X et al (2023) Application of EfficientNet-B0 and GRU-based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions. Cancer Med 12(7):8690\u20138699. https:\/\/doi.org\/10.1002\/cam4.5581","journal-title":"Cancer Med"},{"issue":"2","key":"217_CR20","doi-asserted-by":"publisher","first-page":"548","DOI":"10.3390\/DIAGNOSTICS12020548","volume":"12","author":"BJ Cho","year":"2022","unstructured":"Cho BJ et al (2022) Automated diagnosis of cervical intraepithelial neoplasia in histology images via deep learning. Diagnostics 12(2):548. https:\/\/doi.org\/10.3390\/DIAGNOSTICS12020548","journal-title":"Diagnostics"},{"issue":"2","key":"217_CR21","doi-asserted-by":"publisher","first-page":"298","DOI":"10.3390\/pathogens12020298","volume":"12","author":"S Choi","year":"2023","unstructured":"Choi S, Ismail A, Pappas-Gogos G, Boussios S (2023) HPV and cervical cancer: a review of epidemiology and screening uptake in the UK. Pathogens 12(2):298. https:\/\/doi.org\/10.3390\/pathogens12020298","journal-title":"Pathogens"},{"key":"217_CR22","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1038\/538311a","volume":"538","author":"K Crawford","year":"2016","unstructured":"Crawford K, Calo R (2016) There is a blind spot in AI research. Nature 538:311\u2013313. https:\/\/doi.org\/10.1038\/538311a","journal-title":"Nature"},{"issue":"1","key":"217_CR23","doi-asserted-by":"publisher","first-page":"222","DOI":"10.3390\/ijms21010222","volume":"21","author":"G Curty","year":"2019","unstructured":"Curty G, De Carvalho PS, Soares MA (2019) The role of the Cervicovaginal microbiome on the genesis and as a biomarker of premalignant cervical intraepithelial neoplasia and invasive cervical cancer. Int J Mol Sci 21(1):222. https:\/\/doi.org\/10.3390\/ijms21010222","journal-title":"Int J Mol Sci"},{"key":"217_CR24","doi-asserted-by":"publisher","unstructured":"Davis SE, Greevy RA, Fonnesbeck C, Lasko TA, Walsh CG, Matheny ME (2019) Nonparametric updating method to correct clinical prediction model drift. J Am Med Inform Assoc. https:\/\/doi.org\/10.1093\/jamia\/ocz127","DOI":"10.1093\/jamia\/ocz127"},{"issue":"4","key":"217_CR25","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1002\/ijc.30716","volume":"141","author":"C De Martel","year":"2017","unstructured":"De Martel C, Plummer M, Vignat J, Franceschi S (2017) Worldwide burden of cancer attributable to HPV by site, country and HPV type. Int J Cancer 141(4):664\u2013670. https:\/\/doi.org\/10.1002\/ijc.30716","journal-title":"Int J Cancer"},{"key":"217_CR26","doi-asserted-by":"publisher","unstructured":"Dosovitskiy A et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. CoRR abs\/2010.11929. https:\/\/doi.org\/10.48550\/arXiv.2010.11929","DOI":"10.48550\/arXiv.2010.11929"},{"key":"217_CR27","doi-asserted-by":"publisher","unstructured":"Dyndar OA, Nykoniuk TR, Neimark OS (2021) Modern approaches of cervical intraepithelial neoplasia treatment with the background of genital papillomavirus infection associated with trichomoniasis. In: Medicine and health care in modern society: topical issues and current aspects, pp 48\u201351. https:\/\/doi.org\/10.30525\/978-9934-26-038-4-12","DOI":"10.30525\/978-9934-26-038-4-12"},{"key":"217_CR28","doi-asserted-by":"publisher","unstructured":"Egemen D et al (2023) Artificial intelligence\u2013based image analysis in clinical testing: lessons from cervical cancer screening. JNCI J Natl Cancer Inst:1\u20138. https:\/\/doi.org\/10.1093\/jnci\/djad202","DOI":"10.1093\/jnci\/djad202"},{"key":"217_CR29","doi-asserted-by":"publisher","unstructured":"Fang S, Yang J, Wang M, Liu C, Liu S (2022) An improved image classification method for cervical precancerous lesions based on ShuffleNet. Comput Intell Neurosci 2022. https:\/\/doi.org\/10.1155\/2022\/9675628","DOI":"10.1155\/2022\/9675628"},{"issue":"7","key":"217_CR30","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0288443","volume":"18","author":"A Fazlollahpour-Naghibi","year":"2023","unstructured":"Fazlollahpour-Naghibi A, Bagheri K, Almukhtar M, Taha SR, Zadeh MS, Moghadam KB, Tadi MJ, Rouholamin S, Razavi M, Sepidarkish M, Rostami A (2023) Trichomonas vaginalis infection and risk of cervical neoplasia: a systematic review and meta-analysis. PLoS One 18(7):e0288443. https:\/\/doi.org\/10.1371\/journal.pone.0288443","journal-title":"PLoS One"},{"key":"217_CR31","doi-asserted-by":"publisher","unstructured":"Fu L et al (2022) Deep learning based cervical screening by the cross-modal integration of colposcopy, cytology, and HPV test. Int J Med Inform 159. https:\/\/doi.org\/10.1016\/j.ijmedinf.2021.104675","DOI":"10.1016\/j.ijmedinf.2021.104675"},{"issue":"7","key":"217_CR32","doi-asserted-by":"publisher","first-page":"1791","DOI":"10.1109\/TKDE.2013.118","volume":"26","author":"S Garc\u00eda-Gal\u00e1n","year":"2014","unstructured":"Garc\u00eda-Gal\u00e1n S, Prado RP, Exp\u00f3sito JEM (2014) Swarm fuzzy systems: knowledge acquisition in fuzzy systems and its applications in grid computing. IEEE Trans Knowl Data Eng 26(7):1791\u20131804. https:\/\/doi.org\/10.1109\/TKDE.2013.118","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"217_CR33","doi-asserted-by":"publisher","unstructured":"Garc\u00eda-Gal\u00e1n S, Cabrera JA, Marchewka A, Mu\u00f1oz-Exp\u00f3sito JE, Prado RP, Gal\u00e1n-D\u00e1vila A et al (2021) Interpretable fuzzy rule-based system for fatal ventricular arrhythmia risk level estimation due to QT-prolonging treatments. In: 2021 IEEE International conference on systems, man, and cybernetics (SMC), pp 2810\u20132815. IEEE. https:\/\/doi.org\/10.1109\/SMC52423.2021.9659055","DOI":"10.1109\/SMC52423.2021.9659055"},{"issue":"3","key":"217_CR34","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1016\/j.ygyno.2021.03.025","volume":"161","author":"L Giannella","year":"2021","unstructured":"Giannella L, Giorgi Rossi P, Delli Carpini G, Di Giuseppe J, Bogani G, Gardella B, Monti E, Liverani CA, Ghelardi A, Insinga S, Raspagliesi F, Spinillo A, Vercellini P, Roncella E, Ciavattini A (2021) Age-related distribution of uncommon HPV genotypes in cervical intraepithelial neoplasia grade 3. Gynecol Oncol 161(3):741\u2013747. https:\/\/doi.org\/10.1016\/j.ygyno.2021.03.025","journal-title":"Gynecol Oncol"},{"issue":"3","key":"217_CR35","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1515\/cclm-2018-0658","volume":"57","author":"G Gopal","year":"2019","unstructured":"Gopal G, Suter-Crazzolara C, Toldo L, Eberhardt W (2019) Digital transformation in healthcare\u2013architectures of present and future information technologies. Clin Chem Lab Med (CCLM) 57(3):328\u2013335. https:\/\/doi.org\/10.1515\/cclm-2018-0658","journal-title":"Clin Chem Lab Med (CCLM)"},{"issue":"7","key":"217_CR36","doi-asserted-by":"publisher","first-page":"451","DOI":"10.3390\/diagnostics10070451","volume":"10","author":"P Guo","year":"2020","unstructured":"Guo P et al (2020) Ensemble deep learning for cervix image selection toward improving reliability in automated cervical precancer screening. Diagnostics (Basel) 10(7):451. https:\/\/doi.org\/10.3390\/diagnostics10070451. PMID: 32635269; PMCID: PMC7400120","journal-title":"Diagnostics (Basel)"},{"key":"217_CR37","doi-asserted-by":"publisher","first-page":"5436","DOI":"10.48550\/arXiv.2105.02358","volume":"45","author":"M Guo","year":"2023","unstructured":"Guo M, Liu Z, Mu T, Hu S (2023) Beyond self-attention: external attention using two linear layers for visual tasks. IEEE Trans Pattern Anal Mach Intell 45:5436\u20135447. https:\/\/doi.org\/10.48550\/arXiv.2105.02358","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"12","key":"217_CR38","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1002\/dc.24293","volume":"47","author":"R Gupta","year":"2019","unstructured":"Gupta R, Sodhani P, Mehrotra R, Gupta S (2019) Cervical high-grade squamous intraepithelial lesion on conventional cytology: cytological patterns, pitfalls, and diagnostic clues. Diagn Cytopathol 47(12):1267\u20131276. https:\/\/doi.org\/10.1002\/dc.24293","journal-title":"Diagn Cytopathol"},{"key":"217_CR39","doi-asserted-by":"publisher","first-page":"163","DOI":"10.2147\/MDER.S366303","volume":"15","author":"LW Habtemariam","year":"2022","unstructured":"Habtemariam LW, Zewde ET, Simegn GL (2022) Cervix type and cervical cancer classification system using deep learning techniques. Med Devices (Auckl) 15:163\u2013176. https:\/\/doi.org\/10.2147\/MDER.S366303","journal-title":"Med Devices (Auckl)"},{"issue":"8","key":"217_CR40","doi-asserted-by":"publisher","first-page":"12870","DOI":"10.1002\/jcb.28557","volume":"120","author":"M Hasanzadeh","year":"2019","unstructured":"Hasanzadeh M et al (2019) The interaction of high and low-risk human papillomavirus genotypes increases the risk of developing genital warts: a population-based cohort study. J Cell Biochem 120(8):12870\u201312874. https:\/\/doi.org\/10.1002\/jcb.28557","journal-title":"J Cell Biochem"},{"key":"217_CR41","doi-asserted-by":"publisher","unstructured":"Hassani A et al (2021) Escaping the big data paradigm with compact transformers. CoRR abs\/2104.0. https:\/\/doi.org\/10.48550\/arXiv.2104.05704","DOI":"10.48550\/arXiv.2104.05704"},{"key":"217_CR42","unstructured":"HPV Information Centre (2023) ICO\/IARC Information Centre on HPV and cancer. https:\/\/hpvcentre.net\/hpvatglance.php. Accessed 12 Dec 2023"},{"key":"217_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2020.105589","volume":"30","author":"E Hussain","year":"2020","unstructured":"Hussain E, Mahanta LB, Borah H, Das CR (2020) Liquid based-cytology pap smear dataset for automated multi-class diagnosis of pre-cancerous and cervical cancer lesions. Data Brief 30:105589. https:\/\/doi.org\/10.1016\/j.dib.2020.105589","journal-title":"Data Brief"},{"key":"217_CR44","doi-asserted-by":"publisher","unstructured":"Idlahcen F, Himmi MM, Mahmoudi A (2020) CNN-based approach for cervical cancer classification in whole-slide histopathology images. https:\/\/doi.org\/10.48550\/arXiv.2005.13924","DOI":"10.48550\/arXiv.2005.13924"},{"issue":"7526","key":"217_CR45","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1136\/bmj.38663.459039.7C","volume":"331","author":"I Kalliala","year":"2005","unstructured":"Kalliala I, Anttila A, Pukkala E, Nieminen P (2005) Risk of cervical and other cancers after treatment of cervical intraepithelial neoplasia: retrospective cohort study. BMJ 331(7526):1183\u20131185. https:\/\/doi.org\/10.1136\/bmj.38663.459039.7C","journal-title":"BMJ"},{"issue":"8","key":"217_CR46","doi-asserted-by":"publisher","first-page":"1838","DOI":"10.3390\/diagnostics12081838","volume":"12","author":"Y Karasu-Benyes","year":"2022","unstructured":"Karasu-Benyes Y, Welch EC, Singhal A, Ou J, Tripathi A (2022) A comparative analysis of deep learning models for automated cross-preparation diagnosis of multi-cell liquid pap smear images. Diagnostics (Basel) 12(8):1838. https:\/\/doi.org\/10.3390\/diagnostics12081838. PMID: 36010189; PMCID: PMC9406372","journal-title":"Diagnostics (Basel)"},{"key":"217_CR47","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1038\/s41746-018-0048-y","volume":"1","author":"PA Keane","year":"2018","unstructured":"Keane PA, Topol EJ (2018) With an eye to AI and autonomous diagnosis. NPJ Digit Med 1:40. https:\/\/doi.org\/10.1038\/s41746-018-0048-y","journal-title":"NPJ Digit Med"},{"issue":"1","key":"217_CR48","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1186\/s12916-019-1426-2","volume":"17","author":"CJ Kelly","year":"2019","unstructured":"Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D (2019) Key challenges for delivering clinical impact with artificial intelligence. BMC Med 17(1):195. https:\/\/doi.org\/10.1186\/s12916-019-1426-2","journal-title":"BMC Med"},{"key":"217_CR49","doi-asserted-by":"publisher","unstructured":"Khan A, Han S, Ilyas N, Lee YM, Lee B (2023) CervixFormer: a multi-scale swin transformer-based cervical pap-smear WSI classification framework. Comput Methods Prog Biomed 240. https:\/\/doi.org\/10.1016\/j.cmpb.2023.107718","DOI":"10.1016\/j.cmpb.2023.107718"},{"key":"217_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/healthcare10030468","volume":"10","author":"S Kim","year":"2022","unstructured":"Kim S et al (2022) Role of artificial intelligence interpretation of colposcopic images in cervical cancer screening. Healthcare (Basel) 10:1\u201310. https:\/\/doi.org\/10.3390\/healthcare10030468","journal-title":"Healthcare (Basel)"},{"key":"217_CR51","doi-asserted-by":"publisher","unstructured":"Kondylakis H, Koumakis L, Tsiknakis M, Marias K (2018) Implementing a data management infrastructure for big healthcare data. In: 2018 IEEE EMBS International conference on biomedical health informatics. BHI, pp 361\u2013364. https:\/\/doi.org\/10.1109\/BHI.2018.8333443","DOI":"10.1109\/BHI.2018.8333443"},{"key":"217_CR52","unstructured":"Krause KA, Neelon D, Butler SL (2023) Koilocytosis. In: StatPearls [Internet]. StatPearls Publishing. Accessed 12 Dec 2023"},{"key":"217_CR53","doi-asserted-by":"publisher","first-page":"3762","DOI":"10.1038\/s41598-022-07723-1","volume":"12","author":"M Kruczkowski","year":"2022","unstructured":"Kruczkowski M, Drabik-Kruczkowska A, Marciniak A et al (2022) Predictions of cervical cancer identification by photonic method combined with machine learning. Sci Rep 12:3762. https:\/\/doi.org\/10.1038\/s41598-022-07723-1","journal-title":"Sci Rep"},{"issue":"1","key":"217_CR54","doi-asserted-by":"publisher","first-page":"271","DOI":"10.3390\/ijerph18010271","volume":"18","author":"DH Lee","year":"2021","unstructured":"Lee DH, Yoon S (2021) Application of artificial intelligence-based technologies in the healthcare industry: opportunities and challenges. Int J Environ Res Public Health 18(1):271. https:\/\/doi.org\/10.3390\/ijerph18010271","journal-title":"Int J Environ Res Public Health"},{"issue":"1","key":"217_CR55","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s13027-019-0243-8","volume":"14","author":"Y Liang","year":"2019","unstructured":"Liang Y, Chen M, Qin L, Wan B, Wang H (2019) A meta-analysis of the relationship between vaginal microecology, human papillomavirus infection and cervical intraepithelial neoplasia. Infect Agents Cancer 14(1):29. https:\/\/doi.org\/10.1186\/s13027-019-0243-8","journal-title":"Infect Agents Cancer"},{"key":"217_CR56","doi-asserted-by":"publisher","first-page":"2959","DOI":"10.1049\/ipr2.12531","volume":"16","author":"TL Mahyari","year":"2022","unstructured":"Mahyari TL, Dansereau RM (2022) Multi-layer random walker image segmentation for overlapped cervical cells using probabilistic deep learning methods. IET Image Process 16:2959\u20132972. https:\/\/doi.org\/10.1049\/ipr2.12531","journal-title":"IET Image Process"},{"key":"217_CR57","doi-asserted-by":"publisher","first-page":"41","DOI":"10.25259\/CMAS_03_16_2021","volume":"19","author":"MM Makde","year":"2022","unstructured":"Makde MM, Sathawane P (2022) Liquid-based cytology: technical aspects. Cytojournal 19:41. https:\/\/doi.org\/10.25259\/CMAS_03_16_2021","journal-title":"Cytojournal"},{"key":"217_CR58","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.ejrad.2018.11.009","volume":"110","author":"M Malek","year":"2019","unstructured":"Malek M, Gity M, Alidoosti A, Oghabian Z, Rahimifar P, Seyed Ebrahimi SM et al (2019) A machine learning approach for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted MRI parameters. Eur J Radiol 110:203\u2013211. https:\/\/doi.org\/10.1016\/j.ejrad.2018.11.009","journal-title":"Eur J Radiol"},{"issue":"4","key":"217_CR59","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1097\/PGP.0000000000000617","volume":"39","author":"R Mandal","year":"2020","unstructured":"Mandal R, Ghosh I, Banerjee D, Mittal S, Muwonge R, Roy C, Panda C, Vernekar M, Frappart L, Basu P (2020) Correlation between p16\/Ki-67 expression and the grade of cervical intraepithelial neoplasias. Int J Gynecol Pathol 39(4):384\u2013390. https:\/\/doi.org\/10.1097\/PGP.0000000000000617","journal-title":"Int J Gynecol Pathol"},{"key":"217_CR60","doi-asserted-by":"publisher","unstructured":"Mehta S, Rastegari M (2021) MobileViT: light-weight, general-purpose, and mobile-friendly vision transformer. CoRR abs\/2110.0. https:\/\/doi.org\/10.48550\/arXiv.2110.02178","DOI":"10.48550\/arXiv.2110.02178"},{"key":"217_CR61","series-title":"Lecture notes in computer science","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-12544-8_17","volume-title":"Fuzzy logic and applications. WILF 2018","author":"C Mencar","year":"2019","unstructured":"Mencar C, Alonso JM (2019) Paving the way to explainable artificial intelligence with fuzzy modeling. In: Full\u00e9r R, Giove S, Masulli F (eds) Fuzzy logic and applications. WILF 2018, Lecture notes in computer science, vol 11291. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-12544-8_17"},{"issue":"2","key":"217_CR62","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/s0301-2115(00)00260-8","volume":"90","author":"SP Michalas","year":"2000","unstructured":"Michalas SP (2000) The pap test: George N. Papanicolaou (1883\u20131962) a screening test for the prevention of cancer of uterine cervix. Eur J Obstet Gynecol Reprod Biol 90(2):135\u2013138. https:\/\/doi.org\/10.1016\/s0301-2115(00)00260-8. PMID: 10825631","journal-title":"Eur J Obstet Gynecol Reprod Biol"},{"issue":"8","key":"217_CR63","doi-asserted-by":"publisher","first-page":"C118","DOI":"10.12968\/hmed.2016.77.8.C118","volume":"77","author":"A Mitra","year":"2016","unstructured":"Mitra A, Tzafetas M, Lyons D, Fotopoulou C, Paraskevaidis E, Kyrgiou M (2016) Cervical intraepithelial neoplasia: screening and management. Br J Hosp Med 77(8):C118\u2013C123. https:\/\/doi.org\/10.12968\/hmed.2016.77.8.C118","journal-title":"Br J Hosp Med"},{"issue":"1","key":"217_CR64","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.1038\/s41467-020-15856-y","volume":"11","author":"A Mitra","year":"2020","unstructured":"Mitra A, MacIntyre DA, Ntritsos G, Smith A, Tsilidis KK, Marchesi JR, Bennett PR, Moscicki AB, Kyrgiou M (2020) The vaginal microbiota associates with the regression of untreated cervical intraepithelial neoplasia 2 lesions. Nat Commun 11(1):1999. https:\/\/doi.org\/10.1038\/s41467-020-15856-y","journal-title":"Nat Commun"},{"key":"217_CR65","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1002\/ijc.20244","volume":"111","author":"N Mu\u00f1oz","year":"2004","unstructured":"Mu\u00f1oz N et al (2004) Against which human papillomavirus types shall we vaccinate and screen? The international perspective. Int J Cancer 111:278\u2013285. https:\/\/doi.org\/10.1002\/ijc.20244","journal-title":"Int J Cancer"},{"issue":"10","key":"217_CR66","doi-asserted-by":"publisher","first-page":"195","DOI":"10.3390\/computation11100195","volume":"11","author":"N Nazir","year":"2023","unstructured":"Nazir N, Sarwar A, Saini BS, Shams R (2023) A robust deep learning approach for accurate segmentation of cytoplasm and nucleus in noisy pap smear images. Computation 11(10):195. https:\/\/doi.org\/10.3390\/computation11100195","journal-title":"Computation"},{"key":"217_CR67","doi-asserted-by":"publisher","unstructured":"Nestor B, McDermott MBA, Chauhan G, Naumann T, Hughes MC, Goldenberg A et al (2018) Rethinking clinical prediction: why machine learning must consider year of care and feature aggregation. In: Machine Learning for Health (ML4H): NeurIPS. https:\/\/arxiv.org\/abs\/1811.12583. https:\/\/doi.org\/10.48550\/arXiv.1811.12583","DOI":"10.48550\/arXiv.1811.12583"},{"issue":"9","key":"217_CR68","doi-asserted-by":"publisher","first-page":"2405","DOI":"10.1007\/s11517-023-02835-w","volume":"61","author":"S Nurmaini","year":"2023","unstructured":"Nurmaini S et al (2023) CervicoXNet: an automated cervicogram interpretation network. Med Biol Eng Comput 61(9):2405\u20132416. https:\/\/doi.org\/10.1007\/s11517-023-02835-w","journal-title":"Med Biol Eng Comput"},{"issue":"5","key":"217_CR69","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1080\/01443615.2019.1634030","volume":"40","author":"KS Okunade","year":"2020","unstructured":"Okunade KS (2020) Human papillomavirus and cervical cancer. J Obstet Gynaecol 40(5):602\u2013608","journal-title":"J Obstet Gynaecol"},{"key":"217_CR70","doi-asserted-by":"publisher","unstructured":"Osafo KS, Lin W, Dong B, Sun P (2023) Exploring the interplay between trichomonas vaginalis, human papillomavirus and the microbiota. Gynecol Obstet Clin Med S2667164623000866. https:\/\/doi.org\/10.1016\/j.gocm.2023.10.002","DOI":"10.1016\/j.gocm.2023.10.002"},{"key":"217_CR71","doi-asserted-by":"publisher","DOI":"10.1016\/J.COMPBIOMED.2021.104890","volume":"138","author":"A Pal","year":"2021","unstructured":"Pal A et al (2021) Deep multiple-instance learning for abnormal cell detection in cervical histopathology images. Comput Biol Med 138:104890. https:\/\/doi.org\/10.1016\/J.COMPBIOMED.2021.104890","journal-title":"Comput Biol Med"},{"key":"217_CR72","doi-asserted-by":"publisher","first-page":"S11","DOI":"10.1016\/j.vaccine.2006.05.111","volume":"24","author":"DM Parkin","year":"2006","unstructured":"Parkin DM, Bray F (2006) Chapter 2: the burden of HPV-related cancers. Vaccine 24:S11\u2013S25. https:\/\/doi.org\/10.1016\/j.vaccine.2006.05.111","journal-title":"Vaccine"},{"key":"217_CR73","doi-asserted-by":"publisher","first-page":"8438","DOI":"10.7150\/thno.37187","volume":"9","author":"D Pathania","year":"2019","unstructured":"Pathania D et al (2019) Point-of-care cervical cancer screening using deep learning-based microholography. Theranostics 9:8438\u20138447. https:\/\/doi.org\/10.7150\/thno.37187","journal-title":"Theranostics"},{"key":"217_CR74","doi-asserted-by":"publisher","unstructured":"Peng G, Dong H, Liang T, Li L, Liu J (2021) Diagnosis of cervical precancerous lesions based on multimodal feature changes. Comput Biol Med 130. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104209","DOI":"10.1016\/j.compbiomed.2021.104209"},{"key":"217_CR75","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.puhe.2018.07.012","volume":"164","author":"V Pergialiotis","year":"2018","unstructured":"Pergialiotis V, Pouliakis A, Parthenis C, Damaskou V, Chrelias C, Papantoniou N et al (2018) The utility of artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women. Public Health 164:1\u20136. https:\/\/doi.org\/10.1016\/j.puhe.2018.07.012","journal-title":"Public Health"},{"issue":"1","key":"217_CR76","doi-asserted-by":"publisher","first-page":"97","DOI":"10.3390\/diagnostics11010097","volume":"11","author":"O Plisko","year":"2021","unstructured":"Plisko O, Zodzika J, Jermakova I, Pcolkina K, Prusakevica A, Liepniece-Karele I, Donders et al (2021) Aerobic vaginitis \u2013 underestimated risk factor for cervical intraepithelial neoplasia. Diagnostics 11(1):97. https:\/\/doi.org\/10.3390\/diagnostics11010097","journal-title":"Diagnostics"},{"key":"217_CR77","doi-asserted-by":"publisher","unstructured":"Plissiti ME et al (2018) Sipakmed: a new dataset for feature and image based classification of normal and pathological cervical cells in pap smear images. In: 2018 25th IEEE international conference on image processing (ICIP), pp 3144\u20133148. https:\/\/doi.org\/10.1109\/ICIP.2018.8451588","DOI":"10.1109\/ICIP.2018.8451588"},{"issue":"8","key":"217_CR78","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.3390\/biomedicines9081010","volume":"9","author":"A Popiel","year":"2021","unstructured":"Popiel A, Piotrowska A, Sputa-Grzegrzolka P, Smolarz B, Romanowicz H, Dziegiel P, Podhorska-Okolow M, Kobierzycki C (2021) Preliminary study on the expression of Testin, p16 and Ki-67 in the cervical intraepithelial neoplasia. Biomedicines 9(8):1010. https:\/\/doi.org\/10.3390\/biomedicines9081010","journal-title":"Biomedicines"},{"key":"217_CR79","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e21388","volume":"9","author":"C Rutili de Lima","year":"2023","unstructured":"Rutili de Lima C, Khan SG, Shah SH, Ferri L (2023) Mask region-based CNNs for cervical cancer progression diagnosis on pap smear examinations. Heliyon 9:e21388. https:\/\/doi.org\/10.1016\/j.heliyon.2023.e21388","journal-title":"Heliyon"},{"issue":"3","key":"217_CR80","doi-asserted-by":"publisher","first-page":"337","DOI":"10.4103\/apjon.apjon_15_18","volume":"5","author":"PL Sachan","year":"2018","unstructured":"Sachan PL, Singh M, Patel ML, Sachan R (2018) A study on cervical cancer screening using pap smear test and clinical correlation. Asia Pac J Oncol Nurs 5(3):337\u2013341. https:\/\/doi.org\/10.4103\/apjon.apjon_15_18","journal-title":"Asia Pac J Oncol Nurs"},{"key":"217_CR81","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0118432","volume":"10","author":"T Saito","year":"2015","unstructured":"Saito T, Rehmsmeier M (2015) The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS One 10:e0118432. https:\/\/doi.org\/10.1371\/journal.pone.0118432","journal-title":"PLoS One"},{"issue":"9590","key":"217_CR82","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. Lancet 370(9590):890\u2013907. https:\/\/doi.org\/10.1016\/S0140-6736(07)61416-0","journal-title":"Lancet"},{"issue":"12","key":"217_CR83","doi-asserted-by":"publisher","first-page":"6741","DOI":"10.1007\/s00330-019-06265-x","volume":"29","author":"WC Shen","year":"2019","unstructured":"Shen WC, Chen SW, Wu KC, Hsieh TC, Liang JA, Hung YC et al (2019) Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [F-18]-fluorodeoxyglucose positron emission tomography\/computed tomography. Eur Radiol 29(12):6741\u20136749. https:\/\/doi.org\/10.1007\/s00330-019-06265-x","journal-title":"Eur Radiol"},{"issue":"8","key":"217_CR84","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1016\/j.jacr.2018.04.008","volume":"15","author":"AB Shinagare","year":"2018","unstructured":"Shinagare AB, Balthazar P, Ip IK, Lacson R, Liu J, Ramaiya N et al (2018) High-grade serous ovarian cancer: use of machine learning to predict abdominopelvic recurrence on CT on the basis of serial cancer antigen 125 levels. J Am Coll Radiol 15(8):1133\u20131138. https:\/\/doi.org\/10.1016\/j.jacr.2018.04.008","journal-title":"J Am Coll Radiol"},{"issue":"11","key":"217_CR85","doi-asserted-by":"publisher","first-page":"2623","DOI":"10.3390\/DIAGNOSTICS12112623\/S1","volume":"12","author":"JY Song","year":"2022","unstructured":"Song JY, Im S, Lee SH, Jang HJ (2022) Deep learning-based classification of uterine cervical and endometrial cancer subtypes from whole-slide histopathology images. Diagnostics 12(11):2623. https:\/\/doi.org\/10.3390\/DIAGNOSTICS12112623\/S1","journal-title":"Diagnostics"},{"key":"217_CR01","doi-asserted-by":"publisher","unstructured":"Soper D (2004) Trichomoniasis: under control or undercontrolled? Am J Obstet Gynecol 190(1):281\u2013290. https:\/\/doi.org\/10.1016\/j.ajog.2003.08.023. PMID: 14749674","DOI":"10.1016\/j.ajog.2003.08.023"},{"issue":"1","key":"217_CR86","doi-asserted-by":"publisher","first-page":"40","DOI":"10.4103\/JPI.JPI_50_20","volume":"11","author":"S Sornapudi","year":"2020","unstructured":"Sornapudi S et al (2020) DeepCIN: attention-based cervical histology image classification with sequential feature modeling for pathologist-level accuracy. J Pathol Inform 11(1):40. https:\/\/doi.org\/10.4103\/JPI.JPI_50_20","journal-title":"J Pathol Inform"},{"issue":"2","key":"217_CR87","doi-asserted-by":"publisher","first-page":"56","DOI":"10.4103\/JCLGTP.JCLGTP_17_23","volume":"1","author":"M Swain","year":"2023","unstructured":"Swain M (2023) Update in pathological classification of cervical intraepithelial neoplasia and cervical cancer. J Colposcopy Low Genit Tract Pathol 1(2):56. https:\/\/doi.org\/10.4103\/JCLGTP.JCLGTP_17_23","journal-title":"J Colposcopy Low Genit Tract Pathol"},{"key":"217_CR88","doi-asserted-by":"publisher","unstructured":"Touvron H et al (2021) Training data-efficient image transformers & distillation through attention. In: Proceedings of the 38th International Conference on Machine Learning (eds. Meila, M, Zhang, T), vol. 139, pp 10347\u201310357 (PMLR, 2021). https:\/\/doi.org\/10.48550\/arXiv.2012.12877","DOI":"10.48550\/arXiv.2012.12877"},{"issue":"3","key":"217_CR89","doi-asserted-by":"publisher","first-page":"360","DOI":"10.3390\/jcm8030360","volume":"8","author":"BV Tran","year":"2019","unstructured":"Tran BV et al (2019) Global evolution of research in artificial intelligence in health and medicine: a bibliometric study. J Clin Med 8(3):360. https:\/\/doi.org\/10.3390\/jcm8030360","journal-title":"J Clin Med"},{"key":"217_CR90","doi-asserted-by":"publisher","unstructured":"Trockman A, Kolter JZ (2022) Patches are all you need? CoRR abs\/2201.0. arXiv:2201.09792. https:\/\/doi.org\/10.48550\/arXiv.2201.09792","DOI":"10.48550\/arXiv.2201.09792"},{"key":"217_CR91","unstructured":"WHO (2023) World Health Organization: cervical cancer. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/cervical-cancer?gclid=Cj0KCQiA6vaqBhCbARIsACF9M6k3QLphKnnflyIaSXx-IxQQF4kTXsaDh__MnIZKZuxg7Ob9T9-k0YwaArDMEALw_wcB. Accessed 12 Dec 2023"},{"key":"217_CR92","doi-asserted-by":"publisher","unstructured":"Xu L, Jiang Y, Zhao R (2023) Advances in ablative treatment for human papillomavirus related cervical pre-cancer lesions. Gynecol Obstet Clin Med S266716462300091X. https:\/\/doi.org\/10.1016\/j.gocm.2023.11.002","DOI":"10.1016\/j.gocm.2023.11.002"},{"key":"217_CR93","doi-asserted-by":"publisher","unstructured":"Yan L et al (2021) Multi-state colposcopy image fusion for cervical precancerous lesion diagnosis using BF-CNN. Biomed Signal Process Control 68. https:\/\/doi.org\/10.1016\/j.bspc.2021.102700","DOI":"10.1016\/j.bspc.2021.102700"},{"key":"217_CR02","doi-asserted-by":"publisher","unstructured":"Yang M, Li L, Jiang C, Qin X, Zhou M, Mao X, Xing H (2020) Co-infection with trichomonas vaginalis increases the risk of cervical intraepithelial neoplasia grade 2\u20133 among HPV16 positive female: a large population-based study. BMC Infect Dis 20:642. https:\/\/doi.org\/10.1186\/s12879-020-05349-0","DOI":"10.1186\/s12879-020-05349-0"},{"key":"217_CR94","doi-asserted-by":"publisher","first-page":"6133","DOI":"10.1109\/ACCESS.2023.3235833","volume":"11","author":"N Youneszade","year":"2023","unstructured":"Youneszade N, Marjani M, Pei CP (2023) Deep learning in cervical cancer diagnosis: architecture, opportunities, and open research challenges. IEEE Access 11:6133\u20136149. https:\/\/doi.org\/10.1109\/ACCESS.2023.3235833","journal-title":"IEEE Access"},{"issue":"5","key":"217_CR95","doi-asserted-by":"publisher","first-page":"2172","DOI":"10.1002\/cam4.1471","volume":"7","author":"C Zhang","year":"2018","unstructured":"Zhang C, Liu Y, Gao W, Pan Y, Gao Y, Shen J, Xiong H (2018) The direct and indirect association of cervical microbiota with the risk of cervical intraepithelial neoplasia. Cancer Med 7(5):2172\u20132179. https:\/\/doi.org\/10.1002\/cam4.1471","journal-title":"Cancer Med"},{"issue":"8","key":"217_CR96","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/s10916-019-1356-8","volume":"43","author":"L Zhang","year":"2019","unstructured":"Zhang L, Huang J, Liu L (2019) Improved deep learning network based in combination with cost-sensitive learning for early detection of ovarian cancer in color ultrasound detecting system. J Med Syst 43(8):251. https:\/\/doi.org\/10.1007\/s10916-019-1356-8","journal-title":"J Med Syst"},{"issue":"6","key":"217_CR97","doi-asserted-by":"publisher","first-page":"720","DOI":"10.21147\/j.issn.1000-9604.2020.06.05","volume":"32","author":"S Zhang","year":"2020","unstructured":"Zhang S, Xu H, Zhang L, Qiao Y (2020a) Cervical cancer: epidemiology, risk factors and screening. Chin J Cancer Res 32(6):720\u2013728. https:\/\/doi.org\/10.21147\/j.issn.1000-9604.2020.06.05","journal-title":"Chin J Cancer Res"},{"key":"217_CR98","doi-asserted-by":"publisher","unstructured":"Zhang T et al (2020b) Cervical precancerous lesions classification using pre-trained densely connected convolutional networks with colposcopy images. Biomed Signal Process Control 55. https:\/\/doi.org\/10.1016\/j.bspc.2019.101566","DOI":"10.1016\/j.bspc.2019.101566"},{"key":"217_CR99","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103739","volume":"77","author":"C Zhang","year":"2022","unstructured":"Zhang C, Jia D, Li Z, Wu N (2022) Auxiliary classification of cervical cells based on multi-domain hybrid deep learning framework. Biomed Signal Process Control 77:103739. https:\/\/doi.org\/10.1016\/j.bspc.2022.103739","journal-title":"Biomed Signal Process Control"},{"issue":"9","key":"217_CR100","doi-asserted-by":"publisher","first-page":"6075","DOI":"10.1007\/S00432-022-04446-8\/FIGURES\/5","volume":"149","author":"K Zhang","year":"2023","unstructured":"Zhang K et al (2023) Using deep learning to predict survival outcome in non-surgical cervical cancer patients based on pathological images. J Cancer Res Clin Oncol 149(9):6075\u20136083. https:\/\/doi.org\/10.1007\/S00432-022-04446-8\/FIGURES\/5","journal-title":"J Cancer Res Clin Oncol"}],"container-title":["Interdisciplinary Cancer Research","Gynecological Cancers: An Interdisciplinary Approach"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/16833_2024_217","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T13:51:34Z","timestamp":1745329894000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/16833_2024_217"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031903977","9783031903984"],"references-count":102,"URL":"https:\/\/doi.org\/10.1007\/16833_2024_217","relation":{},"ISSN":["2731-4561","2731-457X"],"issn-type":[{"type":"print","value":"2731-4561"},{"type":"electronic","value":"2731-457X"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"29 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}