RIVA: An Image Dataset of Conventional Pap Smear Cytology with Multiple Independent Annotations

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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Publication Details

Journal
Scientific Data
Published
2025-12-09
DOI
https://doi.org/10.1038/s41597-025-06280-2
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
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article

RIVA: An Image Dataset of Conventional Pap Smear Cytology with Multiple Independent Annotations

Juliana Reves Szemere, Viviana Siless, Luciana Bruno, Emmanuel Iarussi et al.
Scientific Data
AI in cancer detection
article

RIVA: An Image Dataset of Conventional Pap Smear Cytology with Multiple Independent Annotations

Juliana Reves Szemere, Viviana Siless, Luciana Bruno, Emmanuel Iarussi, Marília Santos Andrade, Ricardo Emanuel Vaz Vargas, Paola Ilaria Bianchi, Miles Currie, Alejandro Monastra, J. J. Medel, A. Dos Santos, Bruno Volcovinsky, Sol Anselmo, Estefanía Uelf, Noemí Buosi, Hugo Massaroli
article en

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.

Scientific DataVol. 12(1)
Consejo Nacional de Investigaciones Científicas y Técnicas (AR), National University of General San Martín (AR), Universidad Torcuato Di Tella (AR), Universidad de Buenos Aires (AR), National University of General Sarmiento (AR), Bernardino Rivadavia Natural Sciences Museum (AR), Centro Científico Tecnológico - San Juan (AR), Instituto Tecnológico de Buenos Aires (ITBA) (AR), Fundación Ciencias Exactas y Naturales (AR)
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AI in cancer detection
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