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.

通过将噪声图像和图像到图像对抗生成网络相结合，提出了一种新的数据增强方法，能够增强磁共振样本的真实性和多样性，经过 CNN 的肿瘤检测结果表明，该方法可以显著提高经典数据增强技术的敏感度和同时对其他医学影像任务有显著提升。