Deep Learning for Pathology Detection in Chest X-Rays
In recent years, Deep Learning techniques like Convolutional Neural Networks (CNN), received considerable attention in many scientific domains from computer vision to speech processing. CNN-based approaches hold leading positions in scientific contest such as the "ImageNet Large Scale Visual Recognition Competition", while the applicability of the techniques in the medical domain is subject of ongoing research.
The aim of this project is to investigate and develop techniques for the analysis of chest radiographs with a special focus on Deep Learning. Our objective is to apply deep learning methods to high-resolution medical x-ray images. In the long-term, we are pursuing the goal to employ deep learning methods in order to find patterns between medical reports and medical x-ray images that will help to push the state-of-the-art in computer-aided diagnosis for chest x-ray images.
Using deep learning to solve the following tasks in a cascaded approach (get as far as possible until end of thesis work):
- Normal or Abnormal classification in chest X-ray scans
- Benchmark against existing detection algorithms
- Pathology classification (Infiltrate, effusion, stasis, tumor, pneumothorax, heart size, foreign objects)
- Integrate other information (CRP value, medical history, …) than pixel values
- Evaluate self-trained vs. pre-trained deep learning networks to solve above mentioned tasks
Prof. Dr.-Ing. Tobias Knopp, Institute of Biomedical Imaging