DXA scanning technology stands out for its minimal radiation exposure, positioning it as a prime candidate for frequent, early-stage screening of cardiovascular diseases and other conditions by identifying structural irregularities. Despite this, DXA scans are often plagued by issues of noise and low resolution.
The goal of this project is to create advanced explainable medical image processing and Machine Learning algorithms capable of detecting and localising these irregularities. Embodying a truly multidisciplinary endeavour, this work synthesizes elements of Artificial Intelligence, Machine Learning, Cardiometabolic Health, and Mineralisation Disorders. Our team is able to corroborate the research findings and algorithm effectiveness through population-wide studies.
We are looking for a PhD candidate with a strong academic background in fields such as Computer Science or Engineering. They should excel in programming languages like Python and possess a solid grasp of mathematics, particularly in statistics. Prior research experience and a broad understanding of artificial intelligence and machine learning techniques are essential.
This project is well suited to students who demonstrate critical thinking, creativity, and strong communication skills, and who have an interest in medical image processing. Ideal candidates will be collaborative, adaptable to emerging technologies, and motivated by a passion for advancing knowledge in artificial intelligence and machine learning.
Contact: Dr Syed Zulqarnain Gilani (s.gilani@ecu.edu.au)