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Training data is the key component in designing algorithms for medical imageanalysis and in many cases it is the main bottleneck in achieving good results.Recent progress in image generation has enabled the training of neural networkbased solutions using synthetic data. A key factor in the generation of newsamples is controlling the important appearance features and potentially beingable to generate a new sample of a specific class with different variants. Inthis work we suggest the synthesis of new data by mixing the class specifiedand unspecified representation of different factors in the training data. Ourexperiments on liver lesion classification in CT show an average improvement of7.4% in accuracy over the baseline training scheme.

Improving CNN Training using Disentanglement for Liver Lesion Classification in CT