  One of the challenges of using machine learning techniques with medical data
is the frequent dearth of source image data on which to train. A representative
example is automated lung cancer diagnosis, where nodule images need to be
classified as suspicious or benign. In this work we propose an automatic
synthetic lung nodule image generator. Our 3D shape generator is designed to
augment the variety of 3D images. Our proposed system takes root in autoencoder
techniques, and we provide extensive experimental characterization that
demonstrates its ability to produce quality synthetic images.
