Medical image analysis using deep neural networks has been actively studied. For accurate training of deep neural networks, the learning data should be sufficient and have good quality and generalized characteristics. However, in medical images, it is difficult to acquire sufficient patient data because of the difficulty of patient recruitment, the burden of annotation of lesions by experts, and the invasion of patients’ privacy. In comparison, the medical images of healthy volunteers can be easily acquired. To resolve this data bias problem, the proposed method synthesizes brain tumor images from normal brain images.

中文翻译：

---

脑肿瘤多对比度MR图像的合成以改善数据增强

使用深度神经网络的医学图像分析已得到积极研究。为了精确地训练深度神经网络，学习数据应该足够并且具有良好的质量和广义的特征。然而，在医学图像中，由于患者招募的困难，专家对病灶的注释的负担以及患者隐私的侵害而难以获取足够的患者数据。相比之下，可以轻松获取健康志愿者的医学图像。为了解决该数据偏差问题，所提出的方法从正常脑图像合成脑肿瘤图像。