Primary supervisor
Jianfei CaiDeep learning has achieved ground-breaking performance in many vision tasks in the recent years. The objective of this project is to apply the state-of-the-art deep learning based image classification/detection networks such as ResNet or Faster RCNN for classifying CT or X-Ray images.
This is a "research project" best for students who are independent and willing to take up challenges with high expectation in the grade when fulfilled the somewhat challenging requirements. Under-performing is likely to fail to obtain the passing requirements. It is also a good practice for students who wish to pursue further study at a postgraduate/PhD level.
Student cohort
Aim/outline
The objective of this project is to apply the state-of-the-art deep learning based image classification/detection networks such as ResNet or Faster RCNN for classifying CT or X-Ray images and detecting abnormal regions. It is for the purpose of replacing the current human inspection for automatically, quickly, and safely classifying and detecting abnormal medical images.
URLs/references
https://arxiv.org/abs/1907.04052
Required knowledge
The student must have knowledge on deep learning (e.g. taking online Stanford deep learning, computer vision related courses) and is skilful in Python programming.