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Cervical whole-slide images dataset for multiclass classification.

Mahnaz Mohammadi1, Christina Fell1, David Morrison1

  • 1School of Medicine, University of St Andrews, North Haugh, St Andrews, KY16 9TF, United Kingdom.

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|November 29, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a large dataset of annotated cervical biopsy images for machine learning. This resource enables automated analysis of slides, improving cervical cancer diagnosis and patient triage.

Keywords:
cervical cancercervixdeep learningdigital image databasehealth care datasethistopathologymachine learningwhole-slide imaging

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Area of Science:

  • Digital pathology
  • Computational pathology
  • Machine learning in healthcare

Background:

  • Cervical cancer diagnosis relies on manual review of cytology and biopsy specimens.
  • Histological examination of biopsy slides presents a significant workload for pathologists.
  • Lack of large, annotated datasets hinders the development of AI tools for diagnostic assistance.

Purpose of the Study:

  • To create and share a comprehensive dataset of annotated cervical biopsy whole-slide images.
  • To facilitate the development and validation of machine learning algorithms for cervical cancer diagnosis.
  • To address the obstacle of limited annotated data in computational pathology.

Main Methods:

  • Compiled a dataset of 2,539 whole-slide images from cervical biopsy specimens.
  • Images were annotated manually by multiple pathologists, with consensus on diagnoses and features.
  • Annotations were provided in Jason format, corresponding to iSyntax whole-slide images.

Main Results:

  • The dataset includes detailed annotations at the subslide level.
  • Each image is assigned a consensus diagnosis and subcategory label.
  • This dataset is unique in its scale and level of annotation for public access.

Conclusions:

  • The dataset enabled the development of a model for accurate diagnostic prediction.
  • Automated triaging of biopsy specimens can expedite identification of significant pathologies.
  • This resource supports research in computer vision for human tissue diagnosis.