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However, most publicly available datasets consist of pre-segmented single cell images, contain on-image annotations that must be manually edited out, or are prepared using the conventional Pap smear method. Multicellular liquid Pap image datasets are a more accurate reflection of current cervical screening techniques. While a multicellular liquid SurePath\u2122 dataset has been created, machine learning models struggle to classify a test image set when it is prepared differently from the training set due to visual differences. Therefore, this dataset of multicellular Pap smear images prepared with the more common ThinPrep\u00ae protocol is presented as a helpful resource for training and testing artificial intelligence models, particularly for future application in cervical dysplasia diagnosis. The \u201cBrown Multicellular ThinPrep\u201d (BMT) dataset is the first publicly available multicellular ThinPrep\u00ae dataset, consisting of 600 clinically vetted images collected from 180 Pap smear slides from 180 patients, classified into three key diagnostic categories.<\/jats:p>","DOI":"10.1038\/s41597-024-04328-3","type":"journal-article","created":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T11:46:02Z","timestamp":1735386362000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["BMT: A Cross-Validated ThinPrep Pap Cervical Cytology Dataset for Machine Learning Model Training and Validation"],"prefix":"10.1038","volume":"11","author":[{"given":"E. Celeste","family":"Welch","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenhao","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C. James","family":"Sung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cunxian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anubhav","family":"Tripathi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joyce","family":"Ou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,28]]},"reference":[{"key":"4328_CR1","doi-asserted-by":"publisher","first-page":"e197","DOI":"10.1016\/S2214-109X(22)00501-0","volume":"11","author":"D Singh","year":"2023","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, e197\u2013e206 (2023).","journal-title":"Lancet Glob Health"},{"key":"4328_CR2","first-page":"229","volume":"112","author":"A Buskwofie","year":"2020","unstructured":"Buskwofie, A., David-West, G. & Clare, C. A. A Review of Cervical Cancer: Incidence and Disparities. J Natl Med Assoc 112, 229\u2013232 (2020).","journal-title":"J Natl Med Assoc"},{"key":"4328_CR3","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1097\/COC.0000000000000264","volume":"41","author":"DX Yang","year":"2018","unstructured":"Yang, D. X., Soulos, P. R., Davis, B., Gross, C. P. & Yu, J. B. Impact of Widespread Cervical Cancer Screening. American Journal of Clinical Oncology 41, 289\u2013294 (2018).","journal-title":"American Journal of Clinical Oncology"},{"key":"4328_CR4","doi-asserted-by":"publisher","first-page":"24","DOI":"10.25259\/CMAS_03_05_2021","volume":"19","author":"M Kamal","year":"2022","unstructured":"Kamal, M. Pap Smear Collection and Preparation: Key Points. 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