  Insufficient training data and severe class imbalance are often limiting
factors when developing machine learning models for the classification of rare
diseases. In this work, we address the problem of classifying bone lesions from
X-ray images by increasing the small number of positive samples in the training
set. We propose a generative data augmentation approach based on a
cycle-consistent generative adversarial network that synthesizes bone lesions
on images without pathology. We pose the generative task as an image-patch
translation problem that we optimize specifically for distinct bones (humerus,
tibia, femur). In experimental results, we confirm that the described method
mitigates the class imbalance problem in the binary classification task of bone
lesion detection. We show that the augmented training sets enable the training
of superior classifiers achieving better performance on a held-out test set.
Additionally, we demonstrate the feasibility of transfer learning and apply a
generative model that was trained on one body part to another.
