{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T18:56:53Z","timestamp":1768417013989,"version":"3.49.0"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031070044","type":"print"},{"value":"9783031070051","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-07005-1_1","type":"book-chapter","created":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T14:02:41Z","timestamp":1653141761000},"page":"3-13","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Cleaning Highly Unbalanced Multisource Image Dataset for Quality Control in Cervical Precancer Screening"],"prefix":"10.1007","author":[{"given":"Zhiyun","family":"Xue","sequence":"first","affiliation":[]},{"given":"Peng","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Sandeep","family":"Angara","sequence":"additional","affiliation":[]},{"given":"Anabik","family":"Pal","sequence":"additional","affiliation":[]},{"given":"Jose","family":"Jeronimo","sequence":"additional","affiliation":[]},{"given":"Kanan T.","family":"Desai","sequence":"additional","affiliation":[]},{"given":"Olusegun K.","family":"Ajenifuja","sequence":"additional","affiliation":[]},{"given":"Clement A.","family":"Adepiti","sequence":"additional","affiliation":[]},{"given":"Silvia D.","family":"Sanjose","sequence":"additional","affiliation":[]},{"given":"Mark","family":"Schiffman","sequence":"additional","affiliation":[]},{"given":"Sameer","family":"Antani","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,22]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1016\/j.ajog.2006.01.091","volume":"195","author":"J Jeronimo","year":"2006","unstructured":"Jeronimo, J., Schiffman, M.: Colposcopy at a crossroads. Am. J. Obstet. Gynecol. 195, 349\u2013353 (2006)","journal-title":"Am. J. Obstet. Gynecol."},{"key":"1_CR2","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1093\/jnci\/djy225","volume":"111","author":"L Hu","year":"2019","unstructured":"Hu, L., et al.: An observational study of deep learning and automated evaluation of cervical images for cancer screening. J. Natl. Cancer Inst. 111, 923\u2013932 (2019)","journal-title":"J. Natl. Cancer Inst."},{"key":"1_CR3","doi-asserted-by":"publisher","first-page":"2416","DOI":"10.1002\/ijc.33029","volume":"147","author":"Z Xue","year":"2020","unstructured":"Xue, Z., et al.: A demonstration of automated visual evaluation of cervical images taken with a smartphone camera. Int. J. Cancer 147, 2416\u20132423 (2020)","journal-title":"Int. J. Cancer"},{"key":"1_CR4","doi-asserted-by":"publisher","first-page":"53266","DOI":"10.1109\/ACCESS.2021.3069346","volume":"9","author":"A Pal","year":"2021","unstructured":"Pal, A., et al.: Deep metric learning for cervical image classification. IEEE Access 9, 53266\u201353275 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3069346","journal-title":"IEEE Access"},{"issue":"5","key":"1_CR5","doi-asserted-by":"publisher","first-page":"953","DOI":"10.3390\/jcm10050953","volume":"10","author":"P Guo","year":"2021","unstructured":"Guo, P., et al.: Network visualization and pyramidal feature comparison for ablative treatability classification using digitized cervix images. J. Clin. Med. 10(5), 953 (2021). https:\/\/doi.org\/10.3390\/jcm10050953","journal-title":"J. Clin. Med."},{"key":"1_CR6","doi-asserted-by":"publisher","unstructured":"Guo, P., et al.: Ensemble deep learning for cervix image selection toward improving reliability in automated cervical precancer screening. Diagnostics (Basel, Switz.) 10(7), 451 (2020). https:\/\/doi.org\/10.3390\/diagnostics10070451","DOI":"10.3390\/diagnostics10070451"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Guo, P., Xue, Z., Long, L.R., Antani, S.: Deep learning for assessing image focus for automated cervical cancer screening. In: Proceedings of the IEEE International Conference on Biomedical and Health Informatics, Chicago, IL, USA, 19\u201322 May 2019 (2019)","DOI":"10.1109\/BHI.2019.8834495"},{"key":"1_CR8","doi-asserted-by":"publisher","unstructured":"Digiovanni, S.L., Guaragnella, C., Rizzi, M., Falagario, M.: Healthcare system: a digital green filter for smart health early cervical cancer diagnosis. In: IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), Bologna, Italy, pp. 1\u20136 (2016). https:\/\/doi.org\/10.1109\/RTSI.2016.7740564","DOI":"10.1109\/RTSI.2016.7740564"},{"key":"1_CR9","unstructured":"Sellors, J.W., Sankaranarayanan, R. (eds.): An introduction to colposcopy: indications for colposcopy, instrumentation, principles and documentation of results. Colposcopy and treatment of cervical intraepithelial neoplasia: a beginners\u2019 manual. https:\/\/screening.iarc.fr\/colpochap.php?lang=1&chap=4"},{"issue":"3","key":"1_CR10","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1109\/JBHI.2019.2922682","volume":"24","author":"Z Yue","year":"2020","unstructured":"Yue, Z., et al.: Automatic CIN grades prediction of sequential cervigram image using LSTM with multistate CNN features. IEEE J. Biomed. Health Inform. 24(3), 844\u2013854 (2020). https:\/\/doi.org\/10.1109\/JBHI.2019.2922682","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"1_CR11","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s13027-020-00324-5","volume":"15","author":"KT Desai","year":"2020","unstructured":"Desai, K.T., et al.: Design and feasibility of a novel program of cervical screening in Nigeria: self-sampled HPV testing paired with visual triage. Infect. Agents Cancer 15, 60 (2020). https:\/\/doi.org\/10.1186\/s13027-020-00324-5","journal-title":"Infect. Agents Cancer"},{"key":"1_CR12","doi-asserted-by":"publisher","unstructured":"Lin, T., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, pp. 2999\u20133007 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.324","DOI":"10.1109\/ICCV.2017.324"},{"key":"1_CR13","unstructured":"Chalapathy, R., Chawla, S.: Deep learning for anomaly detection: a survey. https:\/\/arxiv.org\/abs\/1901.03407"},{"key":"1_CR14","unstructured":"Ruff, L., et al.: Deep semi-supervised anomaly detection. In: The International Conference on Learning Representations (ICLR) (2020)"},{"key":"1_CR15","unstructured":"Ruff, L., et al.: Deep one-class classification. In: Proceedings of the 35th International Conference on Machine Learning, PMLR, vol. 80, pp. 4393\u20134402 (2018)"},{"key":"1_CR16","unstructured":"Zhang, H., et al.: ResNeSt: split-attention networks. https:\/\/arxiv.org\/abs\/2004.08955"},{"key":"1_CR17","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. https:\/\/arxiv.org\/abs\/1611.05431"}],"container-title":["Communications in Computer and Information Science","Recent Trends in Image Processing and Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-07005-1_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T14:02:52Z","timestamp":1653141772000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-07005-1_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031070044","9783031070051"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-07005-1_1","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"22 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RTIP2R","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Recent Trends in Image Processing and Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Msida","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malta","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"rtip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.rtip2r-conference.org\/2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"84","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":"14","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":"23% - 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","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":"5-7","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was held online.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}