Synthetic medical image generation has a huge potential for improving healthcare through many applications, from data augmentation for training machine learning systems to preserving patient privacy. Conditional Adversarial Generative Networks (cGANs) use a conditioning factor to generate images and have shown great success in recent years. Intuitively, the information in an image can be divided into two parts: 1) content which is presented through the conditioning vector and 2) style which is the undiscovered information missing from the conditioning vector. Current practices in using cGANs for medical image generation, only use a single variable for image generation (i.e., content) and therefore, do not provide much flexibility nor control over the generated image. In this work we propose a methodology to learn from the image itself, disentangled representations of style and content, and use this information to impose control over the generation process. In this framework, style is learned in a fully unsupervised manner, while content is learned through both supervised learning (using the conditioning vector) and unsupervised learning (with the inference mechanism). We undergo two novel regularization steps to ensure content-style disentanglement. First, we minimize the shared information between content and style by introducing a novel application of the gradient reverse layer (GRL); second, we introduce a self-supervised regularization method to further separate information in the content and style variables. We show that in general, two latent variable models achieve better performance and give more control over the generated image. We also show that our proposed model (DRAI) achieves the best disentanglement score and has the best overall performance.
翻译:合成医学图像的生成具有通过许多应用改善医疗保健的巨大潜力,从数据增强用于培训机器学习系统的数据强化到保护患者隐私,从培训机器学习系统的数据增强到保护患者隐私,这些合成医学图像的生成具有巨大的潜力。 有条件的自动生成网络(cGANs)使用一个调节因素来生成图像并显示近年来的巨大成功。 自然, 图像中的信息可以分为两个部分:1) 通过调节矢量显示的内容, 2) 风格是调控矢量所缺少的未发现的信息。 目前使用 cGANs 生成医疗图像的做法, 仅使用一个单个变量来生成图像( 即内容), 因此, 不为生成图像提供太多的灵活性或控制 。 在这项工作中,我们提出了一种从图像本身中学习的方法, 对样式和内容进行不相交错的表达, 以完全不统一的方式学习, 同时通过监管学习内容( 使用调控矢量矢量) 和不超超超的学习( 调机制) 来学习内容 。 我们通过两个新型的配置步骤来确保内容的升级化步骤, 引入更深层次的自我递化的递增的递化格式 。