Review of Disentanglement Approaches for Medical Applications: Towards
Solving the Gordian Knot of Generative Models in Healthcare
Abstract
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.
Keywords: Generative Models, Disentanglement, Representation Learning,
Medical Applications