Panoramic Radiograph Generation and Image Reconstruction from Latent Vectors Using a Generative Adversarial Network

被引：0

作者：

Kokomoto, Kazuma ^{[1
]}

Okawa, Rena ^{[2
]}

Nakano, Kazuhiko ^{[2
]}

Nozaki, Kazunori ^{[1
]}

机构：

[1] Osaka Univ, Div Med Informat, Dent Hosp, Osaka, Japan

[2] Osaka Univ, Dept Pediat Dent, Grad Sch Dent, Osaka, Japan

来源：

MEDINFO 2023 - THE FUTURE IS ACCESSIBLE | 2024年 / 310卷

关键词：

Generative adversarial network; anonymization; deep learning; dentistry;

D O I：

10.3233/SHTI231263

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this study, StyleGAN2 was trained with panoramic radiographs, and original images were projected into the latent space of StyleGAN2. The resulting latent vectors were input into StyleGAN2, and corresponding images were generated to reconstruct the original images. The original and reconstructed images were evaluated by pediatric dentists and found to be similar. Our results suggest that StyleGAN2 could be applied to the anonymization and data compression of medical images.

引用

页码：1499 / 1500

页数：2

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