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Synthetic Gastritis Image Generation via Loss Function-Based Conditional PGGAN

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/75023

Title: Synthetic Gastritis Image Generation via Loss Function-Based Conditional PGGAN
Authors: Togo, Ren Browse this author
Ogawa, Takahiro Browse this author →KAKEN DB
Haseyama, Miki Browse this author →KAKEN DB
Keywords: Generative adversarial network
anonymization
deep learning
data sharing
medical image analysis
Issue Date: 1-Jul-2019
Publisher: IEEE
Journal Title: IEEE Access
Volume: 7
Start Page: 87448
End Page: 87457
Publisher DOI: 10.1109/ACCESS.2019.2925863
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 dif cult 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 classi cation, 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 classi cation 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 classi cation task. We show that images generated by the LC-PGGAN are effective for gastritis classi cation using gastric X-ray images and have clinical characteristics of the target symptom.
Rights: https://creativecommons.org/licenses/by/3.0/
Type: article
URI: http://hdl.handle.net/2115/75023
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 藤後 廉

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