Exploiting learning algorithms under scarce data regimes is a limitation and
a reality of the medical imaging field. In an attempt to mitigate the problem,
we propose a data augmentation protocol based on generative adversarial
networks. We condition the networks at a pixel-level (segmentation mask) and at
a global-level information (acquisition environment or lesion type). Such
conditioning provides immediate access to the image-label pairs while
controlling global class specific appearance of the synthesized images. To
stimulate synthesis of the features relevant for the segmentation task, an
additional passive player in a form of segmentor is introduced into the
adversarial game. We validate the approach on two medical datasets: BraTS,
ISIC. By controlling the class distribution through injection of synthetic
images into the training set we achieve control over the accuracy levels of the
datasets' classes.

通过基于生成对抗网络的数据增强协议，我们在像素级别和全局信息水平上对网络进行调节，以控制合成图像的外观，从而解决医学图像领域中被数据稀缺限制的问题，并通过将合成图像注入到训练集中来控制数据集类别的准确性水平。

Red-GAN: 通过有条件生成攻击类别不平衡 - 对皮肤镜检和脑部肿瘤 MRI 的医学图像合成的另一视角

Red-GAN: Attacking class imbalance via conditioned generation. Yet  another perspective on medical image synthesis for skin lesion dermoscopy and  brain tumor MRI