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

arXiv: https://arxiv.org/abs/2512.03854 · HTML: https://mlsanity.com/paper/2512.03854/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 3 Dec 2025.

## Builds on (most cited first, 6 of 6)

- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Automated Gleason Grading of Prostate Biopsies using Deep Learning](https://mlsanity.com/paper/1907.07980/) (2019)
- [Going Deeper through the Gleason Scoring Scale: An Automatic end-to-end System for Histology Prostate Grading and Cribriform Pattern Detection](https://mlsanity.com/paper/2105.10490/) (2021)
- [DiagSet: a dataset for prostate cancer histopathological image classification](https://mlsanity.com/paper/2105.04014/) (2021)
- [Foundation Models -- A Panacea for Artificial Intelligence in Pathology?](https://mlsanity.com/paper/2502.21264/) (2025)
- [Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosis](https://mlsanity.com/paper/2307.05519/) (2023)

## Built on by (most cited first, 3 of 3)

- [Validation of Diagnostic Artificial Intelligence Models for Prostate Pathology in a Middle Eastern Cohort](https://mlsanity.com/paper/2512.17499/) (2025)
- [A Distributional Robustness Margin For Pathology Foundation Models](https://mlsanity.com/paper/2607.25497/) (2026)
- [One-Slide Calibration of Pathology Foundation Models](https://mlsanity.com/paper/2610.08944/) (2026)

## Abstract (arXiv)

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:// doi.org/10.6019/S-BIAD2323).