Dr Maxine is an active researcher and has made important contributions in quantitative image analysis and Computer-Aided Diagnosis (CAD) schemes for breast cancer risk prediction, ovarian cancer prognosis (i.e., new methods to evaluate drug treatment response of ovarian cancer patients), and breast, lung and ovarian cancer detection and classification. She is also currently actively doing research in the field of deep learning applied to breast cancer risk prediction, lung cancer diagnosis in computed tomography (CT) scans, brain magnetic resonance imaging (MRI) images, etc.

Dr Maxine recently developed a new scheme to detect bilateral mammographic density asymmetry between left and right breasts, in order to generate a new short-term risk prediction score that yields significantly higher prediction accuracy at the individual level compared to current fixed prediction models that are based on lifetime risk. The estimated cost of mammography screening in the US and Malaysia is very costly. Thus, her research work in this area has potential both to save lives and reduce screening costs, and is of great importance.

Recently, Dr Maxine's research work in deep learning helped her team and herself to secure a fundamental research grant scheme (FRGS) grant related to lung cancer detection and classification in CT scans. She has also secured several local and international grants related to deep learning, radiomics and cancer risk prediction in medical images. Her expertise and contributions in cancer imaging research has been well recognized by peers in the field. Dr Maxine's research results have been published in peer-reviewed scientific journals in Medical Imaging such as IEEE Transactions on Medical Imaging, Annals of Biomedical Engineering, Medical Physics, Physics in Medicine and Biology, Artificial Intelligence in Medicine, and IEEE Transactions on Biomedical Engineering. She has published more than 50 peer-reviewed articles. She was also awarded the Cum Laude (Best Poster) Award and the Honorable Mention Award for the Computer-Aided Diagnosis and Digital Pathology tracks, respectively of the SPIE Medical Imaging conference.

Dr. Maxine's research field is in medical imaging. For the past 15 years, she has been working to investigate and develop new quantitative imaging (QI) feature based clinical markers that can more effectively phenotype disease (e.g., cancer) risk, development and prognosis, which can thus assist in improving efficacy of disease screening, diagnosis and treatment. In current clinical practice, there is a steadily increasing trend of using more and more medical imaging examinations with high dimension and resolution data. For example, high resolution computed tomography (HRCT) with slice thickness smaller than 1mm has been routinely used to replace conventional X-ray radiography. These HRCT images carry considerable useful information that can phenotype biological and physical properties of the diseases. However, subjective image reading and interpretation by the radiologists are not able to accurately extract such important information due to the large inter-reader variability. As a result, deep learning in the medical image processing field as well as a new field of radiomics has recently been attracting extensive research interest and effort. Her research work as an Academic staff at Monash University, Malaysia Campus and as a Postdoctoral Researcher at the Stephenson Research and Technology Center and University of Pittsburgh Medical Center (UPMC), Pittsburgh fits well with the goal of deep learning and radiomics. She has participated in and carried out a number of pioneer works, in these fields.

• ECE2111 - Signals and Systems
• ECE4076 - Computer Vision