Abstract
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.
Highlights
With the development of image recognition technologies, there have been expectations of their applications to clinical devices in the field of medicine [1]
We propose LC-Progressive Growing GAN (PGGAN), a Loss function-based Conditional Progressive Growing Generative Adversarial Network, in this paper
RELATED WORKS we begin with an explanation of the basic concept of generative adversarial network (GAN) in II-A, and we review more specific relevant works on medical data synthesis in II-B
Summary
With the development of image recognition technologies, there have been expectations of their applications to clinical devices in the field of medicine [1]. Since image generation methods learn the distribution of training data without referring to real images, anonymization of individual information can be realized [15]. We propose LC-PGGAN, a Loss function-based Conditional Progressive Growing Generative Adversarial Network, in this paper. We have designed our method to control the conditional information based on adversarial loss functions, which is the most efficient way for performing training. Conventional one-hot vector representation approaches force the model to train a different domain classification task in the early training stage, our model does not have to perform such a difficult task in the early stage This contributes to the realization of efficient training of the adversarial network. Generating anonymized synthetic medical image data Enabling stable training based on the progressive growing network architecture Controlling conditional information based on the conditional loss function for efficient training.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
