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Explainable AI (XAI) in Medical Imaging

Primary supervisor

David Taniar

Research area

Data Engineering

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.

Required knowledge

You must have experience in Deep Learning, and have strong programming skills. 

Learn more about minimum entry requirements.