ArticleScientific data2024
BMT: A Cross-Validated ThinPrep Pap Cervical Cytology Dataset for Machine Learning Model Training and Validation.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Uncertainty-aware and interpretable deep learning in cytopathology: toward trustworthy AI-powered diagnostics.Virchows Archiv : an international journal of pathology · 2026Review
- UniCAS: A foundation model for cervical cytology screening.Cell reports. Medicine · 2026Article
- Pre-analytical reporting in AI-assisted cervical cytology: a scoping review of data acquisition documentation.Frontiers in artificial intelligence · 2026Review
- Dataset of multi-focus (Z-stack) images derived from liquid-based cervical cancer cytology specimens.F1000Research · 2026Article
- AI in Cervical Cancer Cytology Diagnostics: A Narrative Review of Cutting-Edge Studies.Bioengineering (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.