Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks

Understand

Skeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development.

• In this paper, we describe a fully automated deep learning approach to the problem of bone age assessment using data from Pediatric Bone Age Challenge organized by RSNA 2017.
• The dataset for this competition is consisted of 12.6k radiological images of left hand labeled by the bone age and sex of patients.
• Our approach utilizes several deep learning architectures: U-Net, ResNet-50, and custom VGG-style neural networks trained end-to-end.