  Estimation of bone age from hand radiographs is essential to determine
skeletal age in diagnosing endocrine disorders and depicting the growth status
of children. However, existing automatic methods only apply their models to
test images without considering the discrepancy between training samples and
test samples, which will lead to a lower generalization ability. In this paper,
we propose an adversarial regression learning network (ARLNet) for bone age
estimation. Specifically, we first extract bone features from a fine-tuned
Inception V3 neural network and propose regression percentage loss for
training. To reduce the discrepancy between training and test data, we then
propose adversarial regression loss and feature reconstruction loss to
guarantee the transition from training data to test data and vice versa,
preserving invariant features from both training and test data. Experimental
results show that the proposed model outperforms state-of-the-art methods.
