Predicting Alzheimer's Disease
Supervision by: Christian Wachinger (ai-med.de
Neurodegenerative disorders like Alzheimer’s disease (AD) are devastating brain diseases that severely affect a patient’s quality of life for many years and also comes with great economic costs associated with patient care. A variety of data is collected for studying AD including clinical, genetic, and imaging data, with a special focus on early detection and tracking of AD.
Alzheimer's disease causes severe atrophy (cortical thinning) in the brain, as shown in the image below. Such changes can be measured with magnetic resonance imaging (MRI). In addition, genetic markers can increase the risk for developing AD. In this project, we will use multi-modal data for studying Alzheimer's disease. The goal of this thesis is to develop machine/deep learning techniques for AD prediction that fully integrate multi-modal data.
Credit: National Institute on Aging, National Institutes of Health, License
- We are looking for an enthusiastic student with programming skills (Python)
- Experience in Machine Learning and Image Analysis is a plus.
- Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) is desirable.
If you are interested, please contact Christian Wachinger.
| ProjectForm |
| Title: || Predicting Alzheimer's Disease |
| Abstract: || |
| Student: || |
| Director: || Nassir Navab |
| Supervisor: || Christian Wachinger |
| Type: || Master Thesis |
| Area: || Machine Learning, Medical Imaging |
| Status: || open |
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| Finish: || |
| Thesis (optional): || |
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