Research Topic - School of Information Technology (SOIT)

The below project is open for application until position is filled.

Toward Distribution-Robust Medical Imaging Models in the Wild

While deep learning has shown remarkable performance in medical imaging benchmarks, translating these results to real-world clinical deployment remains challenging. Models trained on data from one hospital or population often fail when applied elsewhere due to distributional shifts. These shifts violate the independently and identically distributed (i.i.d.) assumption, causing significant drops in accuracy, miscalibration, and biased predictions for underrepresented groups. Since acquiring new labeled data is often costly or infeasible due to rare diseases, limited expert availability, and privacy constraints, robust solutions are essential. This PhD project will develop methods for building reliable medical imaging models that generalize across distribution shifts without retraining. The project will focus on automated distributional shift detection and monitoring, invariant and distributionally robust representation learning methods, and deployment-time calibration with uncertainty quantification using approaches such as conformal prediction. The outcome will be a robust pipeline for deploying medical imaging models that remain reliable and fair across diverse real-world clinical settings.

We seek a motivated candidate with a strong background in machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch or TensorFlow) are required. Familiarity with medical imaging data or clinical AI applications is an advantage but not essential. An interest in developing reliable and trustworthy AI systems for healthcare is highly desirable.

Main Supervisor (Malaysia): Dr Lim Chern Hong

Associate Supervisor (Malaysia):  Dr Bisan Alsalibi

Associate Supervisor (Australia): Dr Yasmeen George

How to Apply

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