Accepting PhD Students

PhD projects
Call for Applicants: PhD Candidate in the School of Engineering, Monash University Malaysia

Are you keen to pursue your PhD studies in the areas of artificial intelligence and deep learning pertaining to medical images? Are you interested in writing software/programs to help medical doctors/radiologists in their clinical workflow? Are you interested in writing codes to assist doctors in diagnosing/detecting diseases, such as cancer? This PhD project gives you an opportunity to work on quantitative image analysis and medical image processing on lung computed tomography (CT), full-field digital mammography (FFDM) and/or other medical imaging modalities. The aim/objective is to write software to improve the overall standard of healthcare, as well as assist radiologists in their clinical routine/workflow.

Project Abstract: The current imaging modalities are proceeding to higher and higher resolutions/details, such as two-dimensional (2D) X-ray to 3D computed tomography (CT) imaging for lung cancer screening/diagnosis. Higher resolution imaging and the shortage of radiologists faced in many developing countries including Malaysia means that the current demands on already-overworked radiologists are rapidly and significantly increasing. Thus, quantitative image analysis based methods and Computer-Aided Diagnosis (CAD) schemes are required to assist/help the radiologists by providing objective results/feedback in their assessment of medical images. This project will also analyze a new approach, namely deep learning based methods for the medical image processing field. The expected outcome is software that will improve on the current CAD schemes for lung CT, mammography, and/or other imaging modalities.

Basic Requirements:
1. Must have an Undergraduate Degree or Master Degree in one of the following preferred disciplines: Engineering, Information Technology, Computer Science, Information Systems, Physics, or other related fields, and strong motivation to do high quality research in novel biomedical image analysis related fields. Previous research experiences in related fields and scientific publications in journals/conferences are preferred.
2. Open to Local and International students.
3. Tuition Fee Waiver plus a stipend of RM2600 (Year 1), RM2600 (Year 2) and RM2600 (Year 3).

For further details or to express interest, please contact Dr. Maxine Tan ( This position is available immediately and will remain open until filled. Interested candidates should send a cover letter describing your research interests, CV, and (preferably) contact information of 2 to 3 professional references.


Research activity per year

Personal profile


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.

Research interests

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.

Monash teaching commitment

  • ECE2111 - Signals and Systems
  • ECE4076 - Computer Vision

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being

Education/Academic qualification

Engineering Sciences, Ph.D. , Development of a Feature-Deselective Neuroevolution Method and its Relevance in Medical CAD Applications, Vrije Universiteit Brussel (Free University of Brussels)

Award Date: 12 Jan 2012

Electrical & Electronics Engineering , Master of Engineering with Honors , Lossy compression of images in a Computer-Aided Diagnosis scheme for lung nodule detection, University of Nottingham

Award Date: 7 Jan 2006

Research area keywords

  • Quantitative Image Analysis
  • Deep Learning
  • Machine learning
  • Computer-Aided Diagnosis
  • Cancer risk prediction

Collaborations and top research areas from the last five years

Recent external collaboration on country/territory level. Dive into details by clicking on the dots or