Synthetic Data Generation in the Medical Domain using Generative Adversarial Networks
Synthetic
data generation has become important in the medical field for two
reasons. First, medical data are not widely available to researchers due
to patient privacy protections. Second, in some cases such as rare
diseases, only limited data are available, making diagnosis or treatment
difficult even for experts. Synthetic medical data generation can
address these issues by providing artificial medical data that resembles
real data while not associated with real patients.
The goal of
this thesis is to generate synthetic tabular data relevant to the
medical field using generative adversarial networks (GANs) and to
investigate how synthetic data can improve data analysis (machine
learning) performance compared to cases where only real data are
considered.
Contact person: Dr. Sina Sadeghi <sina.sadeghi@medizin.uni-leipzig.de>
