Mohamed Naser
Assistant Professor
The University of Texas MD Anderson Cancer Center at Houston
Department of Radiation Oncology
My research focuses on developing artificial intelligence (AI), machine learning (ML), and deep learning (DL) models to optimize cancer treatment, predict patient outcomes, and improve clinical decision-making. I specialize in quantitative medical imaging, radiomics, and computational oncology, with a particular emphasis on head and neck cancer (HNC) and applications in radiotherapy, treatment toxicity reduction, and risk stratification.
Current projects include deep learning-based tumor segmentation in PET/CT and multiparametric MRI, radiopathomic risk stratification using uncertainty-aware AI models, image-based normal tissue injury and lymphedema/fibrosis severity assessment, and automated sarcopenia assessment for personalized treatment planning.
Students joining my lab will gain hands-on experience in machine learning for medical imaging, AI model development and validation, uncertainty quantification, and biomedical data science. Tutorials may involve training deep learning models for normal tissue and tumor segmentation, developing AI-powered biomarkers for treatment response prediction, or implementing computational pipelines for large-scale clinical imaging analysis. Students will acquire skills in Python-based AI frameworks (PyTorch), radiomics feature extraction, and interdisciplinary collaboration in precision oncology research.
Education & Training
PhD, McMaster University, 2010