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Research
Recent Works
June 04, 2026
AcceptedAcceptedAcceptedAdversarial Vulnerability and Robustness of Deep Learning Models for Panoramic Dental X-ray Segmentation
Our research work on Adversarial Vulnerability and Robustness of Deep Learning Models for Panoramic Dental X-ray Segmentation has been accepted at Scientific Reports journal
May 25, 2026
SubmittedSubmittedSubmittedPaper Submitted
Submitted our work Do LLM Agents Respect Therapeutic Boundaries? Evaluating Cross-System Drug Substitution Hallucination on EMNLP
November, 2025
AcceptedAcceptedAcceptedMulti-Strategy Optimization of U-Net Variants for Orthopantomogram Segmentation
Our paper titled Multi-Strategy Optimization of U-Net Variants for Orthopantomogram Segmentation has been accepted at the 4th IEEE Conference on Biomedical Engineering, Computer and Information Technology for Health 2025 (IEEE BECITHCON 2025)
2025
OngoingOngoingOngoingNational Cybersecurity Authority (NCA) Cybersecurity Research & Innovation Pioneers Grant
Awarded the prestigious NCA Cybersecurity Research & Innovation Pioneers Grant by the National Cybersecurity Authority of the Kingdom of Saudi Arabia for the research proposal "Privacy-Preserving Federated Learning Platform for the Healthcare Domain".
Selected Publications
TFFM: Topology-Aware Feature Fusion Module via Latent Graph Reasoning for Retinal Vessel Segmentation
I. Ahmed, S. Absar, A. A. Sami, S. Sakib, D. Biswas, S. A. M. Mostafa
P2P-CV Workshop @ WACV 2026 |WACV
TL;DR:
Standard retinal vessel segmentation models produce fragmented, disconnected vessels, limiting reliable clinical analysis. Built a topology-aware model that preserves vascular connectivity using graph-based feature fusion and topology-driven loss functions—achieving SOTA performance while reducing vessel fragmentation by ~38%.
Multi-Model Attentional Fusion Ensemble for Accurate Skin Cancer Classification
I. Ahmed, B. Bushon Routh, M. S. Rahman Kohinoor, S. Sakib, M. Mahfuzur Rahman, F. Azzedin
IEEE Access 2024 |Q1
TL;DR:
Skin cancer classification models struggle with diverse lesion types, image artifacts, and dataset imbalance, limiting diagnostic reliability. Built an attention-enhanced ensemble combining ResNet50V2, MobileNetV2, and EfficientNetV2, along with robust preprocessing for artifact removal—achieving superior precision/recall and strong performance across challenging lesion classes.
All publications: