117 citations · 135 across the 7 of their papers we have counts for
7 papers
WAPP: Safe Learning of Positive Security WAF Policies from Live Traffic
Heba Osama, Zeyad Ahmed, Mohamed Amgad +4
Web Application Firewalls (WAFs) mainly rely on signatures to detect known attacks, which can leave gaps against modified or previously unseen payloads. Positive security provides…
Enhanced Web Payload Classification Using WAMM: An AI-Based Framework for Dataset Refinement and Model Evaluation
Heba Osama, Omar Elebiary, Youssef Qassim +4
Web applications increasingly face evasive and polymorphic attack payloads, yet traditional web application firewalls (WAFs) based on static rule sets such as the OWASP Core Rule S…
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning
Lantian Zhang, Mohamed Amgad, Lee A. D. Cooper
Data labeling is often the most challenging task when developing computational pathology models. Pathologist participation is necessary to generate accurate labels, and the limitat…
NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation
Mohamed Amgad, Lamees A. Atteya, Hagar Hussein +34
High-resolution mapping of cells and tissue structures provides a foundation for developing interpretable machine-learning models for computational pathology. Deep learning algorit…
A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study
Sarah N Dudgeon, Si Wen, Matthew G Hanna +19
Purpose: In this work, we present a collaboration to create a validation dataset of pathologist annotations for algorithms that process whole slide images (WSIs). We focus on data…
HistomicsML2.0: Fast interactive machine learning for whole slide imaging data
Sanghoon Lee, Mohamed Amgad, Deepak R. Chittajallu +6
Extracting quantitative phenotypic information from whole-slide images presents significant challenges for investigators who are not experienced in developing image analysis algori…
Learning Clinical Outcomes from Heterogeneous Genomic Data Sources
Safoora Yousefi, Amirreza Shaban, Mohamed Amgad +2
Translating the vast data generated by genomic platforms into reliable predictions of clinical outcomes remains a critical challenge in realizing the promise of genomic medicine la…