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Deep Learning for Clinical Image Denoising

Primary supervisor

Jackie Rong


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

Double Semester


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.