{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T04:14:24Z","timestamp":1745986464136,"version":"3.40.4"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031876592","type":"print"},{"value":"9783031876608","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-87660-8_20","type":"book-chapter","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T12:06:17Z","timestamp":1745928377000},"page":"268-278","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Pixel-Based Anchor Approach for\u00a0Nuclei Detection in\u00a0Cervical Cytology Imaging"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8751-8605","authenticated-orcid":false,"given":"Ciro","family":"Russo","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6030-6904","authenticated-orcid":false,"given":"Yusuf B.","family":"Tanriverdi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2895-6544","authenticated-orcid":false,"given":"Alessandro","family":"Bria","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0840-7350","authenticated-orcid":false,"given":"Claudio","family":"Marrocco","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,30]]},"reference":[{"key":"20_CR1","doi-asserted-by":"publisher","unstructured":"Ahmady\u00a0Phoulady, H., Mouton, P.R.: A new cervical cytology dataset for nucleus detection and image classification (Cervix93) and methods for cervical nucleus detection. arXiv e-prints (2018). https:\/\/doi.org\/10.48550\/arXiv.1811.09651","DOI":"10.48550\/arXiv.1811.09651"},{"key":"20_CR2","doi-asserted-by":"publisher","unstructured":"Braz, E., Lotufo, R.: Nuclei detection using deep learning (2017). https:\/\/doi.org\/10.14209\/sbrt.2017.48","DOI":"10.14209\/sbrt.2017.48"},{"key":"20_CR3","doi-asserted-by":"publisher","DOI":"10.1155\/2012\/101536","volume":"2012","author":"S Chen","year":"2012","unstructured":"Chen, S., Zhao, M., Wu, G., Yao, C., Zhang, J.: Recent advances in morphological cell image analysis. Comput. Math. Methods Med. 2012, 101536 (2012). https:\/\/doi.org\/10.1155\/2012\/101536","journal-title":"Comput. Math. Methods Med."},{"key":"20_CR4","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.: Robust whole slide image analysis for cervical cancer screening using deep learning. Nat. Commun. 12, 5639 (2021). https:\/\/doi.org\/10.1038\/s41467-021-25296-x","journal-title":"Nat. Commun."},{"issue":"20","key":"20_CR5","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., Vasconcelos, M.: A review of computational methods for cervical cells segmentation and abnormality classification. Int. J. Mol. Sci. 20(20), 5114 (2019). https:\/\/doi.org\/10.3390\/ijms20205114","journal-title":"Int. J. Mol. Sci."},{"key":"20_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115642","volume":"185","author":"DN Diniz","year":"2021","unstructured":"Diniz, D.N., et al.: An ensemble method for nuclei detection of overlapping cervical cells. Expert Syst. Appl. 185, 115642 (2021). https:\/\/doi.org\/10.1016\/j.eswa.2021.115642","journal-title":"Expert Syst. Appl."},{"key":"20_CR7","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256. JMLR Workshop and Conference Proceedings (2010)"},{"key":"20_CR8","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"20_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102691","volume":"84","author":"H Jiang","year":"2023","unstructured":"Jiang, H., Zhou, Y., Lin, Y., Chan, R., Liu, J., Chen, H.: Deep learning for computational cytology: a survey. Med. Image Anal. 84, 102691 (2023). https:\/\/doi.org\/10.1016\/j.media.2022.102691","journal-title":"Med. Image Anal."},{"issue":"2","key":"20_CR10","doi-asserted-by":"publisher","first-page":"2687","DOI":"10.1007\/s10462-023-10588-z","volume":"56","author":"P Jiang","year":"2023","unstructured":"Jiang, P., et al.: A systematic review of deep learning-based cervical cytology screening: from cell identification to whole slide image analysis. Artif. Intell. Rev. 56(2), 2687\u20132758 (2023). https:\/\/doi.org\/10.1007\/s10462-023-10588-z","journal-title":"Artif. Intell. Rev."},{"key":"20_CR11","unstructured":"Jocher, G., Chaurasia, A., Qiu, J.: Ultralytics YOLOv8 (2023). https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"20_CR12","doi-asserted-by":"publisher","unstructured":"Kilic, B., Baykal, E., Ekinci, M., Dogan, H., Ercin, M.E., Ersoz, S.: Automated nuclei detection on pleural effusion cytopathology images using YOLOv3. In: 2019 4th International Conference on Computer Science and Engineering (UBMK), pp. 1\u20135. IEEE (2019). https:\/\/doi.org\/10.1109\/UBMK.2019.8907125","DOI":"10.1109\/UBMK.2019.8907125"},{"key":"20_CR13","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2017)"},{"key":"20_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106061","volume":"204","author":"Y Liang","year":"2021","unstructured":"Liang, Y., Pan, C., Sun, W., Liu, Q., Du, Y.: Global context-aware cervical cell detection with soft scale anchor matching. Comput. Methods Programs Biomed. 204, 106061 (2021). https:\/\/doi.org\/10.1016\/j.cmpb.2021.106061","journal-title":"Comput. Methods Programs Biomed."},{"issue":"2","key":"20_CR15","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"TY Lin","year":"2017","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 318\u2013327 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2018.2858826","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"20_CR16","doi-asserted-by":"publisher","first-page":"42","DOI":"10.4103\/2153-3539.192810","volume":"7","author":"C Liu","year":"2016","unstructured":"Liu, C., Shang, F., Ozolek, J.A., Rohde, G.K.: Detecting and segmenting cell nuclei in two-dimensional microscopy images. J. Pathol. Inform. 7(1), 42 (2016). https:\/\/doi.org\/10.4103\/2153-3539.192810","journal-title":"J. Pathol. Inform."},{"key":"20_CR17","doi-asserted-by":"publisher","unstructured":"Lorenzo-Ginori, J.V., Curbelo-Jardines, W., L\u00f3pez-Cabrera, J.D., Huergo-Su\u00e1rez, S.B.: Cervical cell classification using features related to morphometry and texture of nuclei. In: Ruiz-Shulcloper, J., Sanniti\u00a0di Baja, G. (eds.) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp. 222\u2013229. Springer (2013). https:\/\/doi.org\/10.1007\/978-3-642-41827-3-28","DOI":"10.1007\/978-3-642-41827-3-28"},{"key":"20_CR18","doi-asserted-by":"publisher","unstructured":"Ma, D., Liu, J., Li, J., Zhou, Y.: Cervical cancer detection in cervical smear images using deep pyramid inference with refinement and spatial-aware booster. IET Image Process. 14 (2021). https:\/\/doi.org\/10.1049\/iet-ipr.2020.0688","DOI":"10.1049\/iet-ipr.2020.0688"},{"issue":"1717","key":"20_CR19","doi-asserted-by":"publisher","first-page":"9850","DOI":"10.3390\/app13179850","volume":"13","author":"V Mosiichuk","year":"2023","unstructured":"Mosiichuk, V., Sampaio, A., Viana, P., Oliveira, T., Rosado, L.: Improving mobile-based cervical cytology screening: a deep learning nucleus-based approach for lesion detection. Appl. Sci. 13(1717), 9850 (2023). https:\/\/doi.org\/10.3390\/app13179850","journal-title":"Appl. Sci."},{"issue":"4\u20135","key":"20_CR20","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1159\/000477556","volume":"61","author":"R Nayar","year":"2017","unstructured":"Nayar, R., Wilbur, D.C.: The Bethesda system for reporting cervical cytology: a historical perspective. Acta Cytol. 61(4\u20135), 359\u2013372 (2017). https:\/\/doi.org\/10.1159\/000477556","journal-title":"Acta Cytol."},{"issue":"2","key":"20_CR21","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1109\/TITB.2010.2087030","volume":"15","author":"ME Plissiti","year":"2011","unstructured":"Plissiti, M.E., Nikou, C., Charchanti, A.: Automated detection of cell nuclei in pap smear images using morphological reconstruction and clustering. IEEE Trans. Inf. Technol. Biomed. 15(2), 233\u2013241 (2011). https:\/\/doi.org\/10.1109\/TITB.2010.2087030","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"20_CR22","doi-asserted-by":"publisher","unstructured":"Powers, D.M.W.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv (arXiv:2010.16061) (2020). https:\/\/doi.org\/10.48550\/arXiv.2010.16061","DOI":"10.48550\/arXiv.2010.16061"},{"key":"20_CR23","doi-asserted-by":"publisher","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: An Incremental Improvement. arXiv (2018). https:\/\/doi.org\/10.48550\/arXiv.1804.02767","DOI":"10.48550\/arXiv.1804.02767"},{"key":"20_CR24","doi-asserted-by":"publisher","unstructured":"Reis, D., Kupec, J., Hong, J., Daoudi, A.: Real-Time Flying Object Detection with YOLOv8. arXiv (arXiv:2305.09972) (2023). https:\/\/doi.org\/10.48550\/arXiv.2305.09972","DOI":"10.48550\/arXiv.2305.09972"},{"issue":"6","key":"20_CR25","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"20_CR26","doi-asserted-by":"publisher","unstructured":"Russo, C., Bria, A., Marrocco, C.: GravityNet for end-to-end small lesion detection. Artif. Intell. Med. 102842 (2024). https:\/\/doi.org\/10.1016\/j.artmed.2024.102842","DOI":"10.1016\/j.artmed.2024.102842"},{"key":"20_CR27","doi-asserted-by":"publisher","unstructured":"Singh, D., et al.: Global estimates of incidence and mortality of cervical cancer in 2020: a baseline analysis of the WHO Global Cervical Cancer Elimination Initiative. Lancet Glob. Health 11(2), e197\u2013e206 (2023). https:\/\/doi.org\/10.1016\/S2214-109X(22)00501-0","DOI":"10.1016\/S2214-109X(22)00501-0"},{"key":"20_CR28","unstructured":"Society, A.C.: Cancer Facts & Figures 2023. American Cancer Society, Atlanta (2023)"},{"key":"20_CR29","doi-asserted-by":"publisher","unstructured":"Sornapudi, S., et al.: Deep learning nuclei detection in digitized histology images by superpixels. J. Pathol. Inform. 9(1), 5 (2018). https:\/\/doi.org\/10.4103\/jpi.jpi_74_17","DOI":"10.4103\/jpi.jpi_74_17"},{"key":"20_CR30","doi-asserted-by":"publisher","unstructured":"Sung, H., et al.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J. Clin. 71(3), 209\u2013249 (2021). https:\/\/doi.org\/10.3322\/caac.21660","DOI":"10.3322\/caac.21660"},{"key":"20_CR31","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.neucom.2017.01.093","volume":"248","author":"A Tareef","year":"2017","unstructured":"Tareef, A., et al.: Optimizing the cervix cytological examination based on deep learning and dynamic shape modeling. Neurocomputing 248, 28\u201340 (2017). https:\/\/doi.org\/10.1016\/j.neucom.2017.01.093","journal-title":"Neurocomputing"},{"key":"20_CR32","doi-asserted-by":"publisher","unstructured":"Xiang, Y., Sun, W., Pan, C., Yan, M., Yin, Z., Liang, Y.: A novel automation-assisted cervical cancer reading method based on convolutional neural network. Biocybernetics and Biomedical Engineering 40(2), 611\u2013623 (2020). https:\/\/doi.org\/10.1016\/j.bbe.2020.01.016,https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0208521620300218","DOI":"10.1016\/j.bbe.2020.01.016"},{"key":"20_CR33","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.neucom.2021.08.159","volume":"489","author":"X Yu","year":"2022","unstructured":"Yu, X., Wang, J., Hong, Q.Q., Teku, R., Wang, S.H., Zhang, Y.D.: Transfer learning for medical images analyses: a survey. Neurocomputing 489, 230\u2013254 (2022). https:\/\/doi.org\/10.1016\/j.neucom.2021.08.159","journal-title":"Neurocomputing"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition. ICPR 2024 International Workshops and Challenges"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-87660-8_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T12:06:25Z","timestamp":1745928385000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-87660-8_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031876592","9783031876608"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-87660-8_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"30 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}