Deep Generative Model for Longitudinal Analysis
Supervision: Prof. Dr. Nassir Navab,
Dr. Seong Tae Kim
Abstract
Longitudinal analysis of a disease is an important issue to understand its progression as well as to design prognosis and early diagnostic tools. From the longitudinal sample series where data is collected from multiple time points, both the spatial structural abnormalities and the longitudinal variations are captured. Therefore, the temporal dynamics of a disease are more informative than static observations of the symptoms, in particular for neuro-degenerative diseases whose progression span over years with early subtle changes. In this project, we will develop a deep generative method to model the lesion progression over time.
Requirements:
- Good understanding of statistics and machine learning methods.
- Very good programming skills in Python & TensorFlow? / PyTorch?
Location: