Primary supervisor
Jackie RongCo-supervisors
Convolutional neural network (CNN) has exhibited its significance in addressing large-scale vision tasks such as action recognition, image classification, super-resolution and denoising. Recently, many researchers have reported the potential of employing deep learning techniques to refine high-quality clinical images for diagnosis and treatment. Candidates in this research project will have the opportunity to work with real-world clinical data sets to develop novel deep learning methods to solve the problem by learning the relationship between clean and noisy input images. The resulting high-quality images will be further used for specific disease diagnosis.
Student cohort
Aim/outline
The aim of this research project is to design and develop deep learning algorithms to reduce the noise in clinical images for better diagnosis decision-making.
- Exploring real-world clinical data and extract appropriate features.
- Develop novel deep learning models to address the image denoising problem.
- Use the output image to perform clinical diagnosis.
Required knowledge
- Students have knowledge about machine learning and deep learning.
- Student familiar with Python, R, or MATLAB.
- Experience with data processing and image processing is preferred.