  Convolutional Neural Networks (CNNs) achieve excellent computer-assisted
diagnosis with sufficient annotated training data. However, most medical
imaging datasets are small and fragmented. In this context, Generative
Adversarial Networks (GANs) can synthesize realistic/diverse additional
training images to fill the data lack in the real image distribution;
researchers have improved classification by augmenting data with noise-to-image
(e.g., random noise samples to diverse pathological images) or image-to-image
GANs (e.g., a benign image to a malignant one). Yet, no research has reported
results combining noise-to-image and image-to-image GANs for further
performance boost. Therefore, to maximize the DA effect with the GAN
combinations, we propose a two-step GAN-based DA that generates and refines
brain Magnetic Resonance (MR) images with/without tumors separately: (i)
Progressive Growing of GANs (PGGANs), multi-stage noise-to-image GAN for
high-resolution MR image generation, first generates realistic/diverse 256 X
256 images; (ii) Multimodal UNsupervised Image-to-image Translation (MUNIT)
that combines GANs/Variational AutoEncoders or SimGAN that uses a DA-focused
GAN loss, further refines the texture/shape of the PGGAN-generated images
similarly to the real ones. We thoroughly investigate CNN-based tumor
classification results, also considering the influence of pre-training on
ImageNet and discarding weird-looking GAN-generated images. The results show
that, when combined with classic DA, our two-step GAN-based DA can
significantly outperform the classic DA alone, in tumor detection (i.e.,
boosting sensitivity 93.67% to 97.48%) and also in other medical imaging tasks.
