  In this paper, we describe how to apply image-to-image translation techniques
to medical blood smear data to generate new data samples and meaningfully
increase small datasets. Specifically, given the segmentation mask of the
microscopy image, we are able to generate photorealistic images of blood cells
which are further used alongside real data during the network training for
segmentation and object detection tasks. This image data generation approach is
based on conditional generative adversarial networks which have proven
capabilities to high-quality image synthesis. In addition to synthesizing blood
images, we synthesize segmentation mask as well which leads to a diverse
variety of generated samples. The effectiveness of the technique is thoroughly
analyzed and quantified through a number of experiments on a manually collected
and annotated dataset of blood smear taken under a microscope.
