Toward robust histopathology imaging: An unsupervised framework for artifact detection, localization, and restoration

Whole Slide Images are central to modern pathology and computational histopathology. However, tissue processing and slide scanning can introduce artifacts that degrade image quality and hinder computer-aided diagnosis (CAD) systems. Therefore, automated artifact processing has drawn much attention in recent years. Nevertheless, current methods exhibit limitations, including reliance on annotated data and inadequate pixel-level localization capabilities, resulting in fragmented workflows. To address these challenges, we propose an unsupervised automatic pipeline for artifact detection, localization, and restoration. First, based on the features extracted by the histopathology foundation model, anomaly heatmaps are generated using Normalizing Flow to enable precise artifact detection. Subsequently, optimal artifact masks are identified via anomaly heatmaps and unsupervised semantic segmentation for accurate localization. Finally, a mask-guided artifact restoration is performed using a diffusion model. Experimental results demonstrate that our method effectively handles synthetic and real-world artifacts using only normal training data and improves downstream performance on both BCSS segmentation and TCGA-BLCA tumor staging classification, confirming our method's efficacy in bolstering the robustness of CAD systems, reducing manual verification, and providing an automated solution for artifacts in histopathology images.