HARP: Unsupervised Histopathology Artifact Restoration

Moritz Fuchs, Ssharvien Kumar R Sivakumar, Mirko Schöber, Niklas Woltering, Marie-Lisa Eich, Leonille Schweizer, Anirban Mukhopadhyay
Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning, PMLR 250:465-479, 2024.

Abstract

Histopathological analysis, vital for medical diagnostics, is often challenged by artifacts insample preparation and imaging, such as staining inconsistencies and physical obstructions.Addressing this, our work introduces a novel, fully unsupervised histopathological artifactrestoration pipeline (HARP). HARP integrates artifact detection, localization, and restorationinto one pipeline. The first step to make artifact restoration applicable is an analysisof anomaly detection algorithms. Then, HARP leverages the power of unsupervised segmentationtechniques to propose localizations for potential artifacts, for which we select thebest localization based on our novel inpainting denoising diffusion model. Finally, HARPemploys an inpainting model for artifact restoration while conditioning it on the artifact localizations.We evaluate the artifact detection quality along with the image reconstructionquality, surpassing the state-of-the-art artifact restoration. Furthermore, we demonstratethat HARP improves the robustness and reliability of downstream models and show thatpathologists can not tell the difference between clean images and images restored throughHARP. This demonstrates that HARP significantly improves image quality and diagnosticreliability, enhancing histopathological examination accuracy for AI systems.

Cite this Paper

BibTeX
@InProceedings{pmlr-v250-fuchs24a, title = {HARP: Unsupervised Histopathology Artifact Restoration}, author = {Fuchs, Moritz and Sivakumar, Ssharvien Kumar R and Sch\"ober, Mirko and Woltering, Niklas and Eich, Marie-Lisa and Schweizer, Leonille and Mukhopadhyay, Anirban}, booktitle = {Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning}, pages = {465--479}, year = {2024}, editor = {Burgos, Ninon and Petitjean, Caroline and Vakalopoulou, Maria and Christodoulidis, Stergios and Coupe, Pierrick and Delingette, Hervé and Lartizien, Carole and Mateus, Diana}, volume = {250}, series = {Proceedings of Machine Learning Research}, month = {03--05 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v250/main/assets/fuchs24a/fuchs24a.pdf}, url = {https://proceedings.mlr.press/v250/fuchs24a.html}, abstract = {Histopathological analysis, vital for medical diagnostics, is often challenged by artifacts insample preparation and imaging, such as staining inconsistencies and physical obstructions.Addressing this, our work introduces a novel, fully unsupervised histopathological artifactrestoration pipeline (HARP). HARP integrates artifact detection, localization, and restorationinto one pipeline. The first step to make artifact restoration applicable is an analysisof anomaly detection algorithms. Then, HARP leverages the power of unsupervised segmentationtechniques to propose localizations for potential artifacts, for which we select thebest localization based on our novel inpainting denoising diffusion model. Finally, HARPemploys an inpainting model for artifact restoration while conditioning it on the artifact localizations.We evaluate the artifact detection quality along with the image reconstructionquality, surpassing the state-of-the-art artifact restoration. Furthermore, we demonstratethat HARP improves the robustness and reliability of downstream models and show thatpathologists can not tell the difference between clean images and images restored throughHARP. This demonstrates that HARP significantly improves image quality and diagnosticreliability, enhancing histopathological examination accuracy for AI systems.} }
Endnote
%0 Conference Paper %T HARP: Unsupervised Histopathology Artifact Restoration %A Moritz Fuchs %A Ssharvien Kumar R Sivakumar %A Mirko Schöber %A Niklas Woltering %A Marie-Lisa Eich %A Leonille Schweizer %A Anirban Mukhopadhyay %B Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2024 %E Ninon Burgos %E Caroline Petitjean %E Maria Vakalopoulou %E Stergios Christodoulidis %E Pierrick Coupe %E Hervé Delingette %E Carole Lartizien %E Diana Mateus %F pmlr-v250-fuchs24a %I PMLR %P 465--479 %U https://proceedings.mlr.press/v250/fuchs24a.html %V 250 %X Histopathological analysis, vital for medical diagnostics, is often challenged by artifacts insample preparation and imaging, such as staining inconsistencies and physical obstructions.Addressing this, our work introduces a novel, fully unsupervised histopathological artifactrestoration pipeline (HARP). HARP integrates artifact detection, localization, and restorationinto one pipeline. The first step to make artifact restoration applicable is an analysisof anomaly detection algorithms. Then, HARP leverages the power of unsupervised segmentationtechniques to propose localizations for potential artifacts, for which we select thebest localization based on our novel inpainting denoising diffusion model. Finally, HARPemploys an inpainting model for artifact restoration while conditioning it on the artifact localizations.We evaluate the artifact detection quality along with the image reconstructionquality, surpassing the state-of-the-art artifact restoration. Furthermore, we demonstratethat HARP improves the robustness and reliability of downstream models and show thatpathologists can not tell the difference between clean images and images restored throughHARP. This demonstrates that HARP significantly improves image quality and diagnosticreliability, enhancing histopathological examination accuracy for AI systems.
APA
Fuchs, M., Sivakumar, S.K.R., Schöber, M., Woltering, N., Eich, M., Schweizer, L. & Mukhopadhyay, A.. (2024). HARP: Unsupervised Histopathology Artifact Restoration. Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 250:465-479 Available from https://proceedings.mlr.press/v250/fuchs24a.html.

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