  Training data is the key component in designing algorithms for medical image
analysis and in many cases it is the main bottleneck in achieving good results.
Recent progress in image generation has enabled the training of neural network
based solutions using synthetic data. A key factor in the generation of new
samples is controlling the important appearance features and potentially being
able to generate a new sample of a specific class with different variants. In
this work we suggest the synthesis of new data by mixing the class specified
and unspecified representation of different factors in the training data. Our
experiments on liver lesion classification in CT show an average improvement of
7.4% in accuracy over the baseline training scheme.
