HARP: Unsupervised Histopathology Artifact Restoration

Published: 06 Jun 2024, Last Modified: 06 Jun 2024MIDL 2024 PosterEveryoneRevisionsBibTeXCC BY 4.0
Keywords: Histopathology, Artefact Restoration, Diffusion Models, Unsupervised
Abstract: Histopathological analysis, vital for medical diagnostics, is often challenged by artifacts in sample preparation and imaging, such as staining inconsistencies and physical obstructions. Addressing this, our work introduces a novel, fully unsupervised histopathological artifact restoration pipeline (HARP). HARP integrates artifact detection, localization, and restoration into one pipeline. The first step to make artifact restoration applicable is an analysis of anomaly detection algorithms. Then, HARP leverages the power of unsupervised segmentation techniques to propose localizations for potential artifacts, for which we select the best localization based on our novel inpainting denoising diffusion model. Finally, HARP employs an inpainting model for artifact restoration while conditioning it on the artifact localizations. We evaluate the artifact detection quality along with the image reconstruction quality, surpassing the state-of-the-art artifact restoration. Furthermore, we demonstrate that HARP improves the robustness and reliability of downstream models and show that pathologists can not tell the difference between clean images and images restored through HARP. This demonstrates that HARP significantly improves image quality and diagnostic reliability, enhancing histopathological examination accuracy for AI systems.
Latex Code: zip
Copyright Form: pdf
Submission Number: 108
Loading