  Deep neural networks are commonly used for medical purposes such as image
generation, segmentation, or classification. Besides this, they are often
criticized as black boxes as their decision process is often not human
interpretable. Encouraging the latent representation of a generative model to
be disentangled offers new perspectives of control and interpretability.
Understanding the data generation process could help to create artificial
medical data sets without violating patient privacy, synthesizing different
data modalities, or discovering data generating characteristics. These
characteristics might unravel novel relationships that can be related to
genetic traits or patient outcomes. In this paper, we give a comprehensive
overview of popular generative models, like Generative Adversarial Networks
(GANs), Variational Autoencoders (VAEs), and Flow-based Models. Furthermore, we
summarize the different notions of disentanglement, review approaches to
disentangle latent space representations and metrics to evaluate the degree of
disentanglement. After introducing the theoretical frameworks, we give an
overview of recent medical applications and discuss the impact and importance
of disentanglement approaches for medical applications.
