@inproceedings{f847ed6ea442471597a7ef7ec3c80a0e,
title = "BAPGAN: GAN-based bone age progression of femur and phalange x-ray images",
abstract = "Convolutional Neural Networks play a key role in bone age assessment for investigating endocrinology, genetic, and growth disorders under various modalities and body regions. However, no researcher has tackled bone age progression/regression despite its valuable potential applications: bone-related disease diagnosis, clinical knowledge acquisition, and museum education. Therefore, we propose Bone Age Progression Generative Adversarial Network (BAPGAN) to progress/regress both femur/phalange X-ray images while preserving identity and realism. We exhaustively confirm the BAPGAN's clinical potential via F{\'r}echet Inception Distance, Visual Turing Test by two expert orthopedists, and t-Distributed Stochastic Neighbor Embedding.",
keywords = "Age progression, Bone X-ray, Generative adversarial networks, Image synthesis, Visual Turing test",
author = "Shinji Nakazawa and Changhee Han and Jou Hasei and Ryuichi Nakahara and Toshihumi Ozaki",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE.; Medical Imaging 2022: Computer-Aided Diagnosis ; Conference date: 21-03-2022 Through 27-03-2022",
year = "2022",
doi = "10.1117/12.2608065",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Karen Drukker and Iftekharuddin, {Khan M.}",
booktitle = "Medical Imaging 2022",
}