  The shortage of annotated medical images is one of the biggest challenges in
the field of medical image computing. Without a sufficient number of training
samples, deep learning based models are very likely to suffer from over-fitting
problem. The common solution is image manipulation such as image rotation,
cropping, or resizing. Those methods can help relieve the over-fitting problem
as more training samples are introduced. However, they do not really introduce
new images with additional information and may lead to data leakage as the test
set may contain similar samples which appear in the training set. To address
this challenge, we propose to generate diverse images with generative
adversarial network. In this paper, we develop a novel generative method named
generative adversarial U-Net , which utilizes both generative adversarial
network and U-Net. Different from existing approaches, our newly designed model
is domain-free and generalizable to various medical images. Extensive
experiments are conducted over eight diverse datasets including computed
tomography (CT) scan, pathology, X-ray, etc. The visualization and quantitative
results demonstrate the efficacy and good generalization of the proposed method
on generating a wide array of high-quality medical images.
