Dr Mariusz Bajger
Academic Status
College of Science and Engineering
Mariusz Bajger received the M.Sc. in Applied Mathematics degree from the Jagiellonian University in Cracow (Poland) in 1988 and the PhD degree in Mathematics from the University of Queensland in 1996. His research interests include digital health, medical image analysis, data science, network science.
MSc Applied Mathematics, Jagiellonian University, Poland, 1988.
- Digital Health Cooperative Research Centre (DHCRC) Research Grant, 2025-2027
- Commission on Excellence and Innovation in Health (CEIH) Grant, 2023
- Channel 7 Children Research Foundation Grant, 2022
- Freemason’s Centre for Male Health and Wellbeing Grant, 2021-2022
- NeuroSurgical Research Foundation Grant, 2020-2021
- Faculty of Science and Engineering Reinventing Teaching and Learning Grant, 2016
- Channel 7 Children's Research Foundation Grant, 2012-2013
- Lyn Wrigley Breast Cancer Research & Development Fund, Breast Cancer Research Grant, 2011
- Lyn Wrigley Breast Cancer Research & Development Fund, Breast Cancer Research Grant, 2010
- Lyn Wrigley Breast Cancer Research & Development Fund, Breast Cancer Research Grant, 2009
- National Breast Cancer Foundation Grant 2006-2008
- Lyn Wrigley Breast Cancer Research & Development Fund, Breast Cancer Research Grant, 2007
- 3rd prize in M. Kuczma's competition for the best paper in Functional Equations written by Polish author in 2004.
Professional memberships
- Australian Pattern Recognition Society (APRS)
- Association for Computing Machinery (ACM)
- Australian Mathematical Society (AMS)
Journal reviewer (see Publons for details)
- Image Analysis
Recently supervised (or co-supervised) research projects
- Developing deep learning algorithms for Gleason grading of prostate cancer (Zhi Lin, BENG Honours)
- Three-dimensional dual-energy CT baggage scanner image segmentation toolkit (John Dunstan, BENG Honours)
- Feature generation and selection for classification of ROI's in mammography images of dense breasts (Sagar Shrestha, MIT)
- Effects of preprocessing on CNN analysis of breast cancer histopathology images (Kevin Clark, BENG Honours)
- Segmentation methods for digital imaging using shape priors (Ratna Saha, PhD)
- Efficient texture descriptors for detection of masses in dense regions of mammograms (Shelda Sajeev, PhD)
Students interested in research towards Honours, Master or PhD degree in the areas related to those outlined in the above projects please contact Mariusz by email/phone. Other research projects in the area of Medical Image Analysis/Pattern Recognition or Data Science may also be available.
Sturt Rd, Bedford Park
South Australia 5042
South Australia | Northern Territory
Global | Online
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