Artifact-Robust Graph-Based Learning in Digital Pathology
Saba Heidari Gheshlaghi Milan Aryal Nasim Yahyasoltani Masoud Ganji
Whole slide images~(WSIs) are digitized images of tissues placed in glass
slides using advanced scanners. The digital processing of WSIs is challenging
as they are gigapixel images and stored in multi-resolution format. A common
challenge with WSIs is that perturbations/artifacts are inevitable during
storing the glass slides and digitizing them. These perturbations include
motion, which often arises from slide movement during placement, and changes in
hue and brightness due to variations in staining chemicals and the quality of
digitizing scanners. In this work, a novel robust learning approach to account
for these artifacts is presented. Due to the size and resolution of WSIs and to
account for neighborhood information, graph-based methods are called for. We
use graph convolutional network~(GCN) to extract features from the graph
representing WSI. Through a denoiser {and pooling layer}, the effects of
perturbations in WSIs are controlled and the output is followed by a
transformer for the classification of different grades of prostate cancer. To
compare the efficacy of the proposed approach, the model without denoiser is
trained and tested with WSIs without any perturbation and then different
perturbations are introduced in WSIs and passed through the network with the
denoiser. The accuracy and kappa scores of the proposed model with prostate
cancer dataset compared with non-robust algorithms show significant improvement
in cancer diagnosis.