A1 Refereed data article in a scientific journal

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




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

PublisherSpringer Science and Business Media LLC

Publication year2026

Journal: Scientific Data

Article number1061

Volume13

eISSN2052-4463

DOIhttps://doi.org/10.1038/s41597-026-07798-9

Publication's open availability at the time of reportingOpen Access

Publication channel's open availability Open Access publication channel

Web address https://doi.org/10.1038/s41597-026-07798-9

Self-archived copy’s web addresshttps://research.utu.fi/converis/portal/detail/Publication/526992896

Self-archived copy's licenceCC BY

Self-archived copy's versionPublisher`s PDF


Abstract

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.


Downloadable publication

This is an electronic reprint of the original article.
This reprint may differ from the original in pagination and typographic detail. Please cite the original version.




Funding information in the publication
K.K. received funding from the SciLifeLab & Wallenberg Data-Driven Life Science Program (KAW 2024.0159), the Instrumentarium Science Foundation, and the Karolinska Institutet Research Foundation. Open access funding provided by Karolinska Institute.


Last updated on 18/08/2026 02:21:24 PM