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<p>Artificial intelligence (AI) is increasingly used in digital pathology. Publicly available histopathology datasets remain scarce, and those that do exist predominantly represent Western populations. Consequently, the generalizability of AI models to populations from less digitized regions, such as the Middle East, is largely unknown. This motivates the public release of our dataset to support the development and validation of pathology AI models across globally diverse populations. We present 1,017 whole slide images by digitizing 339 glass slides of prostate core needle biopsies from a consecutive series of 185 patients collected in Erbil, Iraq. Each glass slide was scanned by three different whole-slide scanners. The dataset also includes the corresponding Gleason Scores and the International Society of Urological Pathology grades assigned independently by three pathologists. All slides were de-identified and are provided in their native formats without further conversion. The dataset enables grading concordance analyses, color normalization, and cross-scanner robustness evaluations. The dataset is publicly available through the BioImage Archive and released under the CC BY 4.0 license.</p>

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The PAR dataset: Prostate biopsy whole slide images from an underrepresented Middle Eastern population

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月16日

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  • Peshawa J. Muhammad Ali,
  • Navin Vincent,
  • Saman S. Abdulla,
  • Han N. Mohammed Fadhl,
  • Anders Blilie,
  • Kelvin Szolnoky,
  • Julia Anna Mielcarz,
  • Xiaoyi Ji,
  • Kimmo Kartasalo,
  • Abdulbasit K. Al-Talabani,
  • Nita Mulliqi

摘要

Artificial intelligence (AI) is increasingly used in digital pathology. Publicly available histopathology datasets remain scarce, and those that do exist predominantly represent Western populations. Consequently, the generalizability of AI models to populations from less digitized regions, such as the Middle East, is largely unknown. This motivates the public release of our dataset to support the development and validation of pathology AI models across globally diverse populations. We present 1,017 whole slide images by digitizing 339 glass slides of prostate core needle biopsies from a consecutive series of 185 patients collected in Erbil, Iraq. Each glass slide was scanned by three different whole-slide scanners. The dataset also includes the corresponding Gleason Scores and the International Society of Urological Pathology grades assigned independently by three pathologists. All slides were de-identified and are provided in their native formats without further conversion. The dataset enables grading concordance analyses, color normalization, and cross-scanner robustness evaluations. The dataset is publicly available through the BioImage Archive and released under the CC BY 4.0 license.