Muhammad Hamza

PhD Researcher · PhD Researcher

PhD researcher investigating generative adversarial networks and their applications in medical imaging and data augmentation.

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Biography

Muhammad Hamza is a PhD researcher at ICVRL focusing on Generative Adversarial Networks (GANs) for medical image synthesis and augmentation. His research aims to address the challenge of limited labeled data in medical imaging through synthetic data generation.

Research Interests

Generative AI GANs Medical Image Synthesis Data Augmentation Transfer Learning

Education

MS in Computer Science ??? HITEC University Taxila BS in Software Engineering ??? [University Placeholder]

Publications (4)

Journal Article

Deep Attention-Guided Network for Medical Image Segmentation

Shams ur Rehman, Ahmad Khan, Muhammad Hamza

IEEE Transactions on Medical Imaging, 2025

Journal Article

A Comprehensive Survey of Vision Transformers in Medical Imaging

Shams ur Rehman, Muhammad Hamza, Ayesha Malik, Ahmad Khan

ACM Computing Surveys, 2025

Journal Article

GAN-Based Synthetic Data Augmentation for Rare Disease Classification

Muhammad Hamza, Shams ur Rehman, Ayesha Malik

Medical Image Analysis, 2024

Conference Paper

Temporal Action Detection in Surveillance Videos Using Graph Neural Networks

Hassan Ahmed, Muhammad Hamza, Shams ur Rehman

European Conference on Computer Vision (ECCV), 2024

Projects

MedSeg-AI: Intelligent Medical Image Segmentation Platform

Active

An AI-powered platform for automated segmentation of medical images including CT, MRI, and X-ray scans with clinical-grade accuracy.

ActionNet: Video Understanding for Smart Surveillance

Active

Intelligent video analytics system for automated activity detection and anomaly recognition in surveillance footage.

SynMed: Synthetic Medical Data Generation Framework

Completed

A GAN-based framework for generating high-fidelity synthetic medical images to address data scarcity in healthcare AI.