ArticleScientific data2025
A large annotated cervical cytology images dataset for AI models to aid cervical cancer screening.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Accurate Segmentation of Overlapping Cervical Cells Using an Optimized Deep Learning Framework for Cytology Screening.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- Artificial intelligence for colposcopic and cytological image analysis in early cervical cancer detection.iScience · 2026Review
- Pre-analytical reporting in AI-assisted cervical cytology: a scoping review of data acquisition documentation.Frontiers in artificial intelligence · 2026Review
- RIVA: An Image Dataset of Conventional Pap Smear Cytology with Multiple Independent Annotations.Scientific data · 2025Article
- AI in Cervical Cancer Cytology Diagnostics: A Narrative Review of Cutting-Edge Studies.Bioengineering (Basel, Switzerland) · 2025Review
- A large annotated cervical cytology images dataset for AI models to aid cervical cancer screening.Scientific data · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
Accurate detection of abnormal cervical cells in cervical cancer screening increases the chances of timely treatment. The vigorous development of deep learning methods has established a new ecosystem for cervical cancer screening, which has been proven to effectively improve efficiency and accuracy of cell detection in many studies. Although many contributing studies have been conducted, limited public datasets and time-consuming collection efforts may hinder the generalization performance of those advanced models and restrict further research. Through this work, we seek to provide a large dataset of cervical cytology images with exhaustive annotations of abnormal cervical cells. The dataset consists of 8,037 images derived from 129 scanned Thinprep cytologic test (TCT) slide images. Furthermore, we performed evaluation experiments to demonstrate the performance of representative models trained on our dataset in abnormal cells detection.
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Registered trials
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