  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.
