Densely-Connected Deep Learning System for Assessment of Skeletal Maturity

Abstract

Assessment of skeletal maturity plays an essential role in the clinical management of the adolescent disease. This task is very challenging when using machine learning method due to the limited data and large anatomical variations among different subjects. In this paper, we propose a deep learning pipeline to automatically assess the distal radius and ulna (DRU) maturity from left hand radiographs. The model we proposed acquires two convincing advantages: first, our model preserves the maximum information flow and has a much faster convergence rate. Second, our model avoids overfitting even if training with limited data. The proposed method achieves 83.33% and 90.31% for radius and ulna classification respectively.