Automatic Whole-body Bone Age Assessment Using Deep Hierarchical Features
This work addresses bone age assessment for children's growth analysis, but it is incremental as it adapts existing deep learning methods to a new imaging modality.
The paper tackles bone age assessment using whole-body CT images instead of the traditional left-hand X-ray, achieving results through a novel convolutional neural network with additional connections to reduce overfitting on small medical datasets.
Bone age assessment gives us evidence to analyze the children growth status and the rejuvenation involved chronological and biological ages. All the previous works consider left-hand X-ray image of a child in their works. In this paper, we carry out a study on estimating human age using whole-body bone CT images and a novel convolutional neural network. Our model with additional connections shows an effective way to generate a massive number of vital features while reducing overfitting influence on small training data in the medical image analysis research area. A dataset and a comparison with common deep architectures will be provided for future research in this field.