Digital pathology plays a vital role across modern medicine, offering critical insights for disease diagnosis, prognosis, and treatment. However, histopathology images often contain artifacts introduced during slide preparation and digitization. Detecting and excluding them is essential to ensure reliable downstream analysis. Traditional supervised models typically require large annotated datasets, which is resource-intensive and not generalizable to novel artifact types. To address this, we propose DiffusionQC, which detects artifacts as outliers among clean images using a diffusion model. It requires only a set of clean images for training rather than pixel-level artifact annotations and predefined artifact types. Furthermore, we introduce a contrastive learning module to explicitly enlarge the distribution separation between artifact and clean images, yielding an enhanced version of our method. Empirical results demonstrate superior performance to state-of-the-art and offer cross-stain generalization capacity, with significantly less data and annotations.
@article{arxiv.2601.12233,
title = {DiffusionQC: Artifact Detection in Histopathology via Diffusion Model},
author = {Zhenzhen Wang and Zhongliang Zhou and Zhuoyu Wen and Jeong Hwan Kook and John B Wojcik and John Kang},
journal= {arXiv preprint arXiv:2601.12233},
year = {2026}
}