1. BAPGAN: GAN-based Bone Age Progression of Femur and Phalange X-ray Images
- Author
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Nakazawa, Shinji, Han, Changhee, Hasei, Joe, Nakahara, Ryuichi, and Ozaki, Toshifumi
- Subjects
Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - 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 Frechet Inception Distance, Visual Turing Test by two expert orthopedists, and t-Distributed Stochastic Neighbor Embedding., Comment: 6 pages, 5 figures, accepted to SPIE Medical Imaging 2022
- Published
- 2021