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Annotated Pap cell images and smear slices for cell classification.

David Kupas1, Andras Hajdu2, Ilona Kovacs3

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A new dataset, APACC (Annotated Pap cell images and smear slices for Cell Classification), offers over 100,000 images to improve AI-driven cervical cancer screening. This resource aims to overcome limitations in current data for machine learning advancements.

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Area of Science:

  • Biomedical Imaging
  • Computational Pathology
  • Artificial Intelligence in Medicine

Background:

  • Machine learning aids global cervical cancer detection efforts.
  • Current AI research for cervical screening is limited by scarce public image data.
  • Manual analysis of Pap smears is labor-intensive and requires expert interpretation.

Purpose of the Study:

  • To introduce APACC, a large-scale, annotated dataset for cervical cell classification.
  • To address the data scarcity hindering AI development in cervical cancer screening.
  • To provide a comprehensive resource for machine learning model training and validation.

Main Methods:

  • Development of the APACC dataset, comprising 103,675 annotated cell images.
  • Extraction of images from 107 whole Pap smears.
  • Annotation of 21,371 sub-regions within the smears for detailed analysis.

Main Results:

  • The APACC dataset contains a substantial volume of annotated cell images and smear sub-regions.
  • It includes detailed location information for cell images within conventional Pap smears.
  • This dataset facilitates in-depth investigation and study of cervical cells.

Conclusions:

  • APACC significantly expands the available resources for AI-based cervical cancer screening research.
  • The dataset's comprehensive nature supports the development of more accurate and robust machine learning models.
  • APACC is poised to accelerate advancements in automated Pap smear analysis and early cancer detection.