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Adversary-Robust Graph-Based Learning of WSIs
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.
Artifact-Robust Graph-Based Learning in Digital Pathology
Whole slide images~(WSIs) are digitized images of tissues placed in glass slides using advanced scanners.
Explainable and Position-Aware Learning in Digital Pathology
Then, graph-based learning methods can be utilized for the grading and classification of cancer.
Context-Aware Self-Supervised Learning of Whole Slide Images
To assess the performance improvement through self-supervised mechanism, the proposed context-aware model is tested with and without use of pre-trained self-supervised layer.
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