6 citations · 7 across the 2 of their papers we have counts for
6 papers
Adversary-Robust Graph-Based Learning of WSIs
Saba Heidari Gheshlaghi, Milan Aryal, Nasim Yahyasoltani +1
Enhancing the robustness of deep learning models against adversarial attacks is crucial, especially in critical domains like healthcare where significant financial interests height…
Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation
Dibaloke Chanda, Saba Heidari Gheshlaghi, Nasim Yahya Soltani
Despite the success of graph neural networks (GNNs) in various domains, they exhibit susceptibility to adversarial attacks. Understanding these vulnerabilities is crucial for devel…
Artifact-Robust Graph-Based Learning in Digital Pathology
Saba Heidari Gheshlaghi, Milan Aryal, Nasim Yahyasoltani +1
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…
Efficient OCT Image Segmentation Using Neural Architecture Search
Saba Heidari Gheshlaghi, Omid Dehzangi, Ali Dabouei +3
In this work, we propose a Neural Architecture Search (NAS) for retinal layer segmentation in Optical Coherence Tomography (OCT) scans. We incorporate the Unet architecture in the…
A Superpixel Segmentation Based Technique for Multiple Sclerosis Lesion Detection
Saba Heidari Gheshlaghi, Amin Ranjbar, Amir Abolfazl Suratgar +2
A Superpixel Segmentation Based Technique for Multiple Sclerosis Lesion Detection
Segmentation of Multiple Sclerosis lesion in brain MR images using Fuzzy C-Means
Saba Heidari Gheshlaghi, Abolfazl Madani, AmirAbolfazl Suratgar +1
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…