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A multi-center annotated oral cytology dataset for AI-assisted early detection of oral squamous cell carcinoma
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DOI：10.1038/s41597-026-07932-7.png)
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Abstract
En 中文
Oral squamous cell carcinoma is a major public health burden, particularly in regions with limited access to specialist pathology services. Oral brush cytology provides a minimally invasive approach for screening and early assessment, but the development of automated analysis methods requires well-annotated, multi-source datasets. We present a multicenter oral cytology dataset collected from tertiary medical centers in India. The dataset includes Papanicolaou- and May-Grünwald-Giemsa-stained whole-slide images, associated patient- and slide-level metadata, high-resolution image patches, and expert-verified nucleus-level annotations in QuPath-compatible GeoJSON format. The annotations support computational tasks including nucleus segmentation, instance segmentation, and cytological category classification. Dataset-level validation checks are provided to document file completeness, metadata consistency, annotation integrity, patch-to-slide linkage, and reuse readiness. The dataset is intended to facilitate development and benchmarking of computational pathology methods for oral cytology analysis.
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