Authors: Youshan Zhang (Lehigh University)*; Brian D. Davison (Lehigh University)
Abstract: Estimation of bone age from hand radiographs is essential to determine skeletal bone age in diagnosing endocrine disorders and depicting 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 to the test data. 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 general training procedure. 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.