In the past several years, a few cervical Pap smear datasets have been published for use in clinical training. 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™ 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® protocol is presented as a helpful resource for training and testing artificial intelligence models, particularly for future application in cervical dysplasia diagnosis. The "Brown Multicellular ThinPrep" (BMT) dataset is the first publicly available multicellular ThinPrep® dataset, consisting of 600 clinically vetted images collected from 180 Pap smear slides from 180 patients, classified into three key diagnostic categories.
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[PMID: 12075519]
J Natl Med Assoc. 2020 Apr;112(2):229-232
[PMID: 32278478]
