  The annotated medical images are usually expensive to be collected. This
paper proposes a deep learning method on small data to classify Common Imaging
Signs of Lung diseases (CISL) in computed tomography (CT) images. We explore
both the real data and the data generated by Generative Adversarial Network
(GAN) to improve the reliability and the generalization of learning. First, we
use GAN to generate a large number of CISLs from small annotated data, which
are difficult to be distinguished from real counterparts. These generated
samples are used to pre-train a Convolutional Neural Network (CNN) for
classifying CISLs. Second, we fine-tune the CNN classification model with real
data. Experiments were conducted on the LISS database of CISLs. We successfully
convinced radiologists that our generated CISLs samples were real for 56.7% of
our experiments. The pre-trained CNN model achieves 88.4% of mean accuracy of
binary classification, and after fine-tuning, the mean accuracy is
significantly increased to 95.0%. For multi-classification of all types of
CISLs and normal tissues, through the two stages of training, the mean
accuracy, sensitivity and specificity are up to about 91.83%, 92.73% and 99.0%,
respectively. To our knowledge, this is the best result achieved on the LISS
database, which demonstrates that the proposed method is effective and
promising for fulfilling deep learning on small data.
