Artifact Restoration in Histology Images with Diffusion Probabilistic
  Models
Zhenqi He Junjun He Jin Ye Yiqing Shen
Histological whole slide images (WSIs) can be usually compromised by
artifacts, such as tissue folding and bubbles, which will increase the
examination difficulty for both pathologists and Computer-Aided Diagnosis (CAD)
systems. Existing approaches to restoring artifact images are confined to
Generative Adversarial Networks (GANs), where the restoration process is
formulated as an image-to-image transfer. Those methods are prone to suffer
from mode collapse and unexpected mistransfer in the stain style, leading to
unsatisfied and unrealistic restored images. Innovatively, we make the first
attempt at a denoising diffusion probabilistic model for histological artifact
restoration, namely ArtiFusion.Specifically, ArtiFusion formulates the artifact
region restoration as a gradual denoising process, and its training relies
solely on artifact-free images to simplify the training complexity.Furthermore,
to capture local-global correlations in the regional artifact restoration, a
novel Swin-Transformer denoising architecture is designed, along with a time
token scheme. Our extensive evaluations demonstrate the effectiveness of
ArtiFusion as a pre-processing method for histology analysis, which can
successfully preserve the tissue structures and stain style in artifact-free
regions during the restoration. Code is available at
https://github.com/zhenqi-he/ArtiFusion.