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Dr. Maxine is an active researcher who has made significant contributions to quantitative image analysis in the field of Computer-Aided Diagnosis (CAD) schemes, specifically focusing on breast cancer risk prediction and ovarian cancer prognosis. Her research currently emphasizes the application of deep learning techniques for breast cancer risk prediction and lung cancer diagnosis through computed tomography (CT) scans and magnetic resonance imaging (MRI). Dr. Maxine has developed novel schemes to detect bilateral mammographic density asymmetries, leading to improved risk prediction accuracy at the individual level compared to traditional lifetime risk models. Her work addresses the challenges posed by the rising costs of mammography screening in Malaysia and the pressing need for innovative solutions to enhance healthcare outcomes. Throughout her career, Dr. Maxine has secured multiple local and international grants for research related to deep learning and radiomics, solidifying her reputation in the cancer imaging research community. With over 50 publications in top-tier peer-reviewed scientific journals, including the IEEE Transactions on Medical Imaging and Annals of Biomedical Engineering, she is recognized for her impactful contributions. Dr. Maxine holds a Ph.D. from Vrije Universiteit Brussel and has previously served as a postdoctoral researcher at the University of Pittsburgh Medical Center, further honing her expertise in medical imaging research.
Monash University • Malaysia
Teaching and research in medical imaging and quantitative image analysis.
Requirements are standardized across the Faculty of Information Technology for most Master's programs including Computer Science and Data Science.