DiffusionQC: Artifact Detection in Histopathology via Diffusion Model

TL;DR

DiffusionQC is a novel artifact detection method in histopathology images that leverages diffusion models and contrastive learning, requiring only clean images for training and outperforming existing approaches.

Contribution

It introduces a diffusion-based outlier detection framework that eliminates the need for extensive annotations and enhances separation between artifacts and clean images.

Findings

Outperforms state-of-the-art artifact detection methods

Requires significantly less annotated data

Generalizes well across different stain types

Abstract

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.