[Submitted on 3 Dec 2025 (v1), last revised 22 Jul 2026 (this version, v2)]

Authors: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

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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. The slides are associated with Gleason scores and International Society of Urological Pathology grades assigned independently by three pathologists. Scanning was performed using two high-throughput scanners (Leica and Hamamatsu) and one compact scanner (Grundium). 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 PAR dataset has been deposited to the BioImage Archive under accession number S-BIAD2323 (https:// this http URL).

Submission history

From: Nita Mulliqi [view email]
[v1] Wed, 3 Dec 2025 14:54:14 UTC (3,421 KB)
[v2] Wed, 22 Jul 2026 13:14:13 UTC (3,426 KB)