# DiffusionQC: Artifact Detection in Histopathology via Diffusion Model

arXiv: https://arxiv.org/abs/2601.12233 · HTML: https://mlsanity.com/paper/2601.12233/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 18 Jan 2026.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 5 of 5)

- [Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop Training](https://mlsanity.com/paper/2409.19587/) (2024)
- [Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs](https://mlsanity.com/paper/2403.07743/) (2024)
- [PixCell: A generative foundation model for digital histopathology images](https://mlsanity.com/paper/2506.05127/) (2025)
- [HistoART: Histopathology Artifact Detection and Reporting Tool](https://mlsanity.com/paper/2507.00044/) (2025)
- [Pathology-Informed Latent Diffusion Model for Anomaly Detection in Lymph Node Metastasis](https://mlsanity.com/paper/2508.15236/) (2025)

## Built on by (most cited first, 1 of 1)

- [Self-Supervised Contrastive Learning for Cardiac MR Sequence Classification](https://mlsanity.com/paper/2605.24789/) (2026)

## Abstract (arXiv)

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.