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Explainable AI (XAI) in Medical Imaging

Primary supervisor

David Taniar


  • Dr Sicily Ting Fung Fung (Monash University Malaysia)

Medical imaging segmentation

Are you interested in applying your AI/DL knowledge to the medical domain?

This project focuses on the use of AI in Medical Imaging (e.g. CT, MRI, X-Ray, Ultrasound, etc). The work includes segmentation and classification; for example, segmenting tumour from the medical images, and then classify the grade of the tumour. We will use various Deep Learning techniques, such as CNN, and will experiment with a variety of Deep Learning frameworks, such as U-Net, ResNet, etc.

One of the main challenges in medical image segmentation and classification is that the results must be explainable; hence Explainable AI or XAI is essential. We will use a variety of XAI methods, such as Grad-CAM, and others.

This project will involve a lot of experiments using DL/AI methods. We will use the Monash High Performance Computing platform (MASSIVE) to do the experiments.

Student cohort

Double Semester

Required knowledge

You need to have a Deep Learning knowledge, and strong programming skills.