论文标题
红色gan：通过条件发电的攻击类失衡。皮肤病变皮肤镜和脑肿瘤MRI的医学图像合成的另一个观点
Red-GAN: Attacking class imbalance via conditioned generation. Yet another perspective on medical image synthesis for skin lesion dermoscopy and brain tumor MRI
论文作者
论文摘要
在稀缺数据制度下利用学习算法是医学成像领域的限制和现实。为了减轻问题，我们提出了基于生成对抗网络的数据增强协议。我们在像素级（分段掩码）和全球级别信息（获取环境或病变类型）上调节网络。这种条件可立即访问图像标签对，同时控制合成图像的全局类特异性外观。为了刺激与分割任务相关的功能的合成，将以分段形式的其他被动玩家引入对抗性游戏中。我们在两个医疗数据集上验证了该方法：brats，isic。通过将合成图像注入训练集中，通过注入类分布来控制数据集类别的准确性水平。
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.
