{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:59:37Z","timestamp":1786978777874,"version":"build-2736575974"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030009489","type":"print"},{"value":"9783030009496","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"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":[[2018]]},"DOI":"10.1007\/978-3-030-00949-6_11","type":"book-chapter","created":{"date-parts":[[2018,9,13]],"date-time":"2018-09-13T09:09:17Z","timestamp":1536829757000},"page":"86-94","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["DeepCerv: Deep Neural Network for Segmentation Free Robust Cervical Cell Classification"],"prefix":"10.1007","author":[{"given":"O. U.","family":"Nirmal Jith","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. K.","family":"Harinarayanan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Srishti","family":"Gautam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arnav","family":"Bhavsar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anil K.","family":"Sao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,9,14]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2014\/842037","volume":"2014","author":"E Bengtsson","year":"2014","unstructured":"Bengtsson, E., Malm, P.: Screening for Cervical Cancer Using Automated Analysis of PAP-Smears. Computational and Mathematical Methods in Medicine 2014, 1\u201312 (2014)","journal-title":"Computational and Mathematical Methods in Medicine"},{"key":"11_CR2","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.cmpb.2016.10.001","volume":"138","author":"K Bora","year":"2017","unstructured":"Bora, K., Chowdhury, M., Mahanta, L.B., Kundu, M.K., Das, A.K.: Automated classification of Pap smear images to detect cervical dysplasia. Computer Methods and Programs in Biomedicine 138, 31\u201347 (2017)","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"2","key":"11_CR3","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.cmpb.2013.12.012","volume":"113","author":"T Chankong","year":"2014","unstructured":"Chankong, T., Theera-Umpon, N., Auephanwiriyakul, S.: Automatic cervical cell segmentation and classification in Pap smears. Computer Methods and Programs in Biomedicine 113(2), 539\u2013556 (2014)","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"1","key":"11_CR4","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1177\/001316446002000104","volume":"20","author":"J Cohen","year":"1960","unstructured":"Cohen, J.: A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20(1), 37\u201346 (1960)","journal-title":"Educational and Psychological Measurement"},{"issue":"2","key":"11_CR5","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1002\/dc.22817","volume":"41","author":"TM Elsheikh","year":"2013","unstructured":"Elsheikh, T.M., Austin, R.M., Chhieng, D.F., Miller, F.S., Moriarty, A.T., Renshaw, A.A.: American Society of Cytopathology: American Society of Cytopathology workload recommendations for automated Pap test screening: Developed by the productivity and quality assurance in the era of automated screening task force. Diagnostic Cytopathology 41(2), 174\u2013178 (2013)","journal-title":"Diagnostic Cytopathology"},{"key":"11_CR6","doi-asserted-by":"publisher","first-page":"S63","DOI":"10.1016\/j.vaccine.2006.05.113","volume":"24","author":"HC Kitchener","year":"2006","unstructured":"Kitchener, H.C., Castle, P.E., Cox, J.T.: Chapter 7: achievements and limitations of cervical cytology screening. Vaccine 24, S63\u2013S70 (2006)","journal-title":"Vaccine"},{"key":"11_CR7","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems. pp. 1097\u20131105 (2012)"},{"issue":"2","key":"11_CR8","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1109\/JBHI.2016.2519686","volume":"21","author":"Z Lu","year":"2017","unstructured":"Lu, Z., Carneiro, G., Bradley, A.P., Ushizima, D., Nosrati, M.S., Bianchi, A.G.C., Carneiro, C.M., Hamarneh, G.: Evaluation of Three Algorithms for the Segmentation of Overlapping Cervical Cells. IEEE Journal of Biomedical and Health Informatics 21(2), 441\u2013450 (2017)","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"1","key":"11_CR9","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.compbiomed.2008.11.006","volume":"39","author":"Y Marinakis","year":"2009","unstructured":"Marinakis, Y., Dounias, G., Jantzen, J.: Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification. Computers in Biology and Medicine 39(1), 69\u201378 (2009)","journal-title":"Computers in Biology and Medicine"},{"issue":"3","key":"11_CR10","doi-asserted-by":"publisher","first-page":"276","DOI":"10.11613\/BM.2012.031","volume":"22","author":"ML McHugh","year":"2012","unstructured":"McHugh, M.L.: Interrater reliability: The kappa statistic. Biochemia Medica 22(3), 276\u2013282 (2012)","journal-title":"Biochemia Medica"},{"issue":"2","key":"11_CR11","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.artmed.2010.02.006","volume":"49","author":"L Nanni","year":"2010","unstructured":"Nanni, L., Lumini, A., Brahnam, S.: Local binary patterns variants as texture descriptors for medical image analysis. Artificial Intelligence in Medicine 49(2), 117\u2013125 (2010)","journal-title":"Artificial Intelligence in Medicine"},{"key":"11_CR12","unstructured":"Norup, J.: Classification of Pap-Smear Data by Tranduction Neuro-Fuzzy Methods. masterThesis, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark (2005)"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Phoulady, H.A., Zhou, M., Goldgof, D.B., Hall, L.O., Mouton, P.R.: Automatic quantification and classification of cervical cancer via adaptive nucleus shape modeling. In: Image Processing (ICIP), 2016 IEEE International Conference On. pp. 2658\u20132662. IEEE (2016)","DOI":"10.1109\/ICIP.2016.7532841"},{"issue":"1","key":"11_CR14","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s00521-012-1220-y","volume":"24","author":"B Sokouti","year":"2014","unstructured":"Sokouti, B., Haghipour, S., Tabrizi, A.D.: A framework for diagnosing cervical cancer disease based on feedforward MLP neural network and ThinPrep histopathological cell image features. Neural Computing and Applications 24(1), 221\u2013232 (2014)","journal-title":"Neural Computing and Applications"},{"issue":"16","key":"11_CR15","doi-asserted-by":"publisher","first-page":"2114","DOI":"10.1001\/jama.287.16.2114","volume":"287","author":"D Solomon","year":"2002","unstructured":"Solomon, D.: The 2001 Bethesda SystemTerminology for Reporting Results of Cervical Cytology. JAMA 287(16), 2114 (2002)","journal-title":"JAMA"},{"issue":"10","key":"11_CR16","doi-asserted-by":"publisher","first-page":"2421","DOI":"10.1109\/TBME.2015.2430895","volume":"62","author":"Y Song","year":"2015","unstructured":"Song, Y., Zhang, L., Chen, S., Ni, D., Lei, B., Wang, T.: Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning. IEEE transactions on bio-medical engineering 62(10), 2421\u20132433 (2015)","journal-title":"IEEE transactions on bio-medical engineering"},{"issue":"4","key":"11_CR17","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1002\/dc.2840110408","volume":"11","author":"NA Young","year":"1994","unstructured":"Young, N.A., Naryshkin, S., Atkinson, B.F., Ehya, H., Gupta, P.K., Kline, T.S., Luff, R.D.: Interobserver variability of cervical smears with squamous-cell abnormalities: A philadelphia study. Diagnostic cytopathology 11(4), 352\u2013357 (1994)","journal-title":"Diagnostic cytopathology"},{"key":"11_CR18","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.compmedimag.2017.01.002","volume":"56","author":"L Zhang","year":"2017","unstructured":"Zhang, L., Kong, H., Liu, S., Wang, T., Chen, S., Sonka, M.: Graph-based segmentation of abnormal nuclei in cervical cytology. Computerized Medical Imaging and Graphics 56, 38\u201348 (2017)","journal-title":"Computerized Medical Imaging and Graphics"},{"issue":"3","key":"11_CR19","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1002\/cyto.a.22407","volume":"85","author":"Ling Zhang","year":"2013","unstructured":"Zhang, L., Kong, H., Ting Chin, C., Liu, S., Fan, X., Wang, T., Chen, S.: Automation-assisted cervical cancer screening in manual liquid-based cytology with hematoxylin and eosin staining. Cytometry. Part A: The Journal of the International Society for Analytical Cytology 85(3), 214\u2013230 (Mar 2014)","journal-title":"Cytometry Part A"},{"issue":"6","key":"11_CR20","doi-asserted-by":"publisher","first-page":"1633","DOI":"10.1109\/JBHI.2017.2705583","volume":"21","author":"L Zhang","year":"2017","unstructured":"Zhang, L., Lu, L., Nogues, I., Summers, R.M., Liu, S., Yao, J.: DeepPap: Deep Convolutional Networks for Cervical Cell Classification. IEEE Journal of Biomedical and Health Informatics 21(6), 1633\u20131643 (2017)","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Zhu, X., Yao, J., Zhu, F., Huang, J.: Wsisa: Making survival prediction from whole slide histopathological images. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 7234\u20137242 (2017)","DOI":"10.1109\/CVPR.2017.725"}],"container-title":["Lecture Notes in Computer Science","Computational Pathology and Ophthalmic Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-00949-6_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T20:27:50Z","timestamp":1694550470000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-00949-6_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030009489","9783030009496"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-00949-6_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"14 September 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"COMPAY","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Computational Pathology","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Granada","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"compay2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/site\/compaysymposium2018\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"76% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.28","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"n\/a","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}