{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-new-cervical-cytology-dataset-for-nucleus","title":"A New Cervical Cytology Dataset for Nucleus Detection and Image Classification (Cervix93) and Methods for Cervical Nucleus Detection","arxiv_id":"1811.09651","date":"2018-11-23","proceeding":null,"authors":["Hady Ahmady Phoulady","Peter R. Mouton"],"abstract":"Analyzing Pap cytology slides is an important tasks in detecting and grading\nprecancerous and cancerous cervical cancer stages. Processing cytology images\nusually involve segmenting nuclei and overlapping cells. We introduce a\ncervical cytology dataset that can be used to evaluate nucleus detection, as\nwell as image classification methods in the cytology image processing area.\nThis dataset contains 93 real image stacks with their grade labels and manually\nannotated nuclei within images. We also present two methods: a baseline method\nbased on a previously proposed approach, and a deep learning method, and\ncompare their results with other state-of-the-art methods. Both the baseline\nmethod and the deep learning method outperform other state-of-the-art methods\nby significant margins. Along with the dataset, we publicly make the evaluation\ncode and the baseline method available to download for further benchmarking.","url_abs":"http://arxiv.org/abs/1811.09651v1","url_pdf":"http://arxiv.org/pdf/1811.09651v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-new-cervical-cytology-dataset-for-nucleus","repo_url":"https://github.com/parham-ap/cytology_dataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"cervical-nucleus-detection","task_name":"Cervical Nucleus Detection"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[{"slug":"cervix93-cytology-dataset","name":"Cervix93 Cytology Dataset","full_name":null}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}