Adversarial Regression Learning for Bone Age Estimation
This work addresses a domain-specific problem in medical imaging for diagnosing endocrine disorders in children, with incremental improvements over existing methods.
The paper tackles the problem of bone age estimation from hand radiographs by addressing the discrepancy between training and test samples, which improves generalization, and the proposed model outperforms state-of-the-art methods.
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.