  Synthetic medical image generation has a huge potential for improving
healthcare through many applications, from data augmentation for training
machine learning systems to preserving patient privacy. Conditional Adversarial
Generative Networks (cGANs) use a conditioning factor to generate images and
have shown great success in recent years. Intuitively, the information in an
image can be divided into two parts: 1) content which is presented through the
conditioning vector and 2) style which is the undiscovered information missing
from the conditioning vector. Current practices in using cGANs for medical
image generation, only use a single variable for image generation (i.e.,
content) and therefore, do not provide much flexibility nor control over the
generated image. In this work we propose a methodology to learn from the image
itself, disentangled representations of style and content, and use this
information to impose control over the generation process. In this framework,
style is learned in a fully unsupervised manner, while content is learned
through both supervised learning (using the conditioning vector) and
unsupervised learning (with the inference mechanism). We undergo two novel
regularization steps to ensure content-style disentanglement. First, we
minimize the shared information between content and style by introducing a
novel application of the gradient reverse layer (GRL); second, we introduce a
self-supervised regularization method to further separate information in the
content and style variables. We show that in general, two latent variable
models achieve better performance and give more control over the generated
image. We also show that our proposed model (DRAI) achieves the best
disentanglement score and has the best overall performance.
