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
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