Adversary-Robust Graph-Based Learning of WSIs
Saba Heidari Gheshlaghi Milan Aryal Nasim Yahyasoltani Masoud Ganji
Enhancing the robustness of deep learning models against adversarial attacks
is crucial, especially in critical domains like healthcare where significant
financial interests heighten the risk of such attacks. Whole slide images
(WSIs) are high-resolution, digitized versions of tissue samples mounted on
glass slides, scanned using sophisticated imaging equipment. The digital
analysis of WSIs presents unique challenges due to their gigapixel size and
multi-resolution storage format. In this work, we aim at improving the
robustness of cancer Gleason grading classification systems against adversarial
attacks, addressing challenges at both the image and graph levels. As regards
the proposed algorithm, we develop a novel and innovative graph-based model
which utilizes GNN to extract features from the graph representation of WSIs. A
denoising module, along with a pooling layer is incorporated to manage the
impact of adversarial attacks on the WSIs. The process concludes with a
transformer module that classifies various grades of prostate cancer based on
the processed data. To assess the effectiveness of the proposed method, we
conducted a comparative analysis using two scenarios. Initially, we trained and
tested the model without the denoiser using WSIs that had not been exposed to
any attack. We then introduced a range of attacks at either the image or graph
level and processed them through the proposed network. The performance of the
model was evaluated in terms of accuracy and kappa scores. The results from
this comparison showed a significant improvement in cancer diagnosis accuracy,
highlighting the robustness and efficiency of the proposed method in handling
adversarial challenges in the context of medical imaging.