ArticleScientific data2025
RIVA: An Image Dataset of Conventional Pap Smear Cytology with Multiple Independent Annotations.
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 3 papers.
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Who cites it
3 citing papers in PubMed.
- 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
- RIVA: An Image Dataset of Conventional Pap Smear Cytology with Multiple Independent Annotations.Scientific data · 2025Article
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Pap smear remains the primary screening test for cervical cancer in many low-resource regions, yet publicly available image datasets largely feature liquid-based preparations. We introduce RIVA, a high-resolution collection of 959 conventional-smear images (1024 × 1024 px) scanned at 40x magnification, sourced from 115 patients. To ensure label quality, each image was annotated by up to four independent medical professionals, with 42% of the images reviewed by all four, resulting in 26,158 annotations based on the Bethesda classification. Annotations provide coordinates of nuclei and classification labels by up to four annotators. The dataset includes 15,949 unique cells across five (pre)cancerous types (SCC, HSIL, ASCH, LSIL, ASCUS) and three non-lesion categories (NILM, ENDO, INFL). These four-expert annotations not only give RIVA a consensus-driven ground truth for robust AI training but also enable inter-annotator consistency analysis-agreement rates reach 94% for lesion vs. non-lesion and 74% across the full eight-category Bethesda scheme.
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Registered trials
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