Synthetic Gastritis Image Generation via Loss Function-Based Conditional PGGAN
Synthetic Gastritis Image Generation via Loss Function-Based Conditional PGGAN
复制标题
DOI：
10.1109/access.2019.2925863
复制
发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Haseyama, Miki
中科院分区：
文献类型：
--
作者：
Togo, Ren;Ogawa, Takahiro;Haseyama, Miki
本文提出了一种基于生成对抗网络（GAN）模型的合成胃炎图像生成方法。医学影像数据共享是实现诊断支持系统的关键问题。然而，由于医学图像数据中包含个人信息，研究人员仍然难以获得医学图像数据。最近提出的GAN模型可以在不看到真实图像数据的情况下学习训练图像的分布，并且个体信息可以通过生成的图像完全匿名。如果生成的图像可以作为医学图像分类的训练图像，那么促进医学图像分析将是可行的。胃炎是胃癌的危险因素，可通过胃x线影像进行诊断。我们的方法采用图像生成的方法，而不是收集大量的胃x线图像数据。我们提出了一种基于损失函数的条件渐进式生长生成对抗网络（LC-PGGAN），这是一种可以用于胃炎分类问题的胃炎图像生成方法。LC-PGGAN在训练阶段通过增加新的层数，逐渐学习胃x线图像中的胃炎特征。此外，LC-PGGAN采用了基于损失函数的条件对抗学习，生成的图像可以用作胃炎分类任务。我们发现LC-PGGAN生成的图像对胃炎x线图像的分类是有效的，并且具有目标症状的临床特征。
In this paper, a novel synthetic gastritis image generation method based on a generative adversarial network (GAN) model is presented. Sharing medical image data is a crucial issue for realizing diagnostic supporting systems. However, it is still difficult for researchers to obtain medical image data since the data include individual information. Recently proposed GAN models can learn the distribution of training images without seeing real image data, and individual information can be completely anonymized by generated images. If generated images can be used as training images in medical image classification, promoting medical image analysis will become feasible. In this paper, we targeted gastritis, which is a risk factor for gastric cancer and can be diagnosed by gastric X-ray images. Instead of collecting a large amount of gastric X-ray image data, an image generation approach was adopted in our method. We newly propose loss function-based conditional progressive growing generative adversarial network (LC-PGGAN), a gastritis image generation method that can be used for a gastritis classification problem. The LC-PGGAN gradually learns the characteristics of gastritis in gastric X-ray images by adding new layers during the training step. Moreover, the LC-PGGAN employs loss function-based conditional adversarial learning so that generated images can be used as the gastritis classification task. We show that images generated by the LC-PGGAN are effective for gastritis classification using gastric X-ray images and have clinical characteristics of the target symptom.