Registration is open - make plans to attend
Get pricing and details
>
Conference 12033 > Paper 12033-47
Paper 12033-47

BAPGAN: GAN-based bone age progression of femur and phalange X-ray images

In person: 23 February 2022 • 2:00 PM - 2:20 PM PST

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 Fréchet Inception Distance, Visual Turing Test by two expert orthopedists, and t-Distributed Stochastic Neighbor Embedding.

Presenter

LPIXEL Inc. (Japan)
Shinji Nakazawa received the Master’s Degree in Engineering from The University of Tokyo. His research interests include Computer Vision and Machine Learning, especially Deep Learning for Medical Imaging.
Presenter/Author
LPIXEL Inc. (Japan)
Author
Changhee Han
Saitama Prefectural Univ. (Japan)
Author
Okayama City Hospital (Japan)
Author
Okayama Univ. (Japan)
Author
Okayama Univ. (Japan)