  Diversity in data is critical for the successful training of deep learning
models. Leveraged by a recurrent generative adversarial network, we propose the
CT-SGAN model that generates large-scale 3D synthetic CT-scan volumes ($\geq
224\times224\times224$) when trained on a small dataset of chest CT-scans.
CT-SGAN offers an attractive solution to two major challenges facing machine
learning in medical imaging: a small number of given i.i.d. training data, and
the restrictions around the sharing of patient data preventing to rapidly
obtain larger and more diverse datasets. We evaluate the fidelity of the
generated images qualitatively and quantitatively using various metrics
including Fr\'echet Inception Distance and Inception Score. We further show
that CT-SGAN can significantly improve lung nodule detection accuracy by
pre-training a classifier on a vast amount of synthetic data.
