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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.
Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation
A novel explainability-based method is proposed to identify important nodes in the graph and perform edge perturbation between these nodes.
Artifact-Robust Graph-Based Learning in Digital Pathology
Whole slide images~(WSIs) are digitized images of tissues placed in glass slides using advanced scanners.
Efficient OCT Image Segmentation Using Neural Architecture Search
We incorporate the Unet architecture in the NAS framework as its backbone for the segmentation of the retinal layers in our collected and pre-processed OCT image dataset.
Segmentation of Multiple Sclerosis lesion in brain MR images using Fuzzy C-Means
Magnetic resonance images (MRI) play an important role in supporting and substituting clinical information in the diagnosis of multiple sclerosis (MS) disease by presenting lesion in brain MR images.
