  In this paper, we explore the possibility of generating artificial biomedical
images that can be used as a substitute for real image datasets in applied
machine learning tasks. We are focusing on generation of realistic chest X-ray
images as well as on the lymph node histology images using the two recent GAN
architectures including DCGAN and PGGAN. The possibility of the use of
artificial images instead of real ones for training machine learning models was
examined by benchmark classification tasks being solved using conventional and
deep learning methods. In particular, a comparison was made by replacing real
images with synthetic ones at the model training stage and comparing the
prediction results with the ones obtained while training on the real image
data. It was found that the drop of classification accuracy caused by such
training data substitution ranged between 2.2% and 3.5% for deep learning
models and between 5.5% and 13.25% for conventional methods such as LBP +
Random Forests.
