  Generative models have been applied in the medical imaging domain for various
image recognition and synthesis tasks. However, a more controllable and
interpretable image synthesis model is still lacking yet necessary for
important applications such as assisting in medical training. In this work, we
leverage the efficient self-attention and contrastive learning modules and
build upon state-of-the-art generative adversarial networks (GANs) to achieve
an attribute-aware image synthesis model, termed AttributeGAN, which can
generate high-quality histopathology images based on multi-attribute inputs. In
comparison to existing single-attribute conditional generative models, our
proposed model better reflects input attributes and enables smoother
interpolation among attribute values. We conduct experiments on a
histopathology dataset containing stained H&E images of urothelial carcinoma
and demonstrate the effectiveness of our proposed model via comprehensive
quantitative and qualitative comparisons with state-of-the-art models as well
as different variants of our model. Code is available at
https://github.com/karenyyy/MICCAI2021AttributeGAN.
