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Deep learning based medical image classification

Primary supervisor

Jianfei Cai

Deep 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

Double Semester

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.