  Bone age is one of the most important indicators for assessing bone's
maturity, which can help to interpret human's growth development level and
potential progress. In the clinical practice, bone age assessment (BAA) of
X-ray images requires the joint consideration of the appearance and location
information of hand bones. These kinds of information can be effectively
captured by the relation of different anatomical parts of hand bone. Recently
developed methods differ mostly in how they model the part relation and choose
useful parts for BAA. However, these methods neglect the mining of relationship
among different parts, which can help to improve the assessment accuracy. In
this paper, we propose a novel part relation module, which accurately discovers
the underlying concurrency of parts by using multi-scale context information of
deep learning feature representation. Furthermore, based on the part relation,
we explore a new part selection module, which comprehensively measures the
importance of parts and select the top ranking parts for assisting BAA. We
jointly train our part relation and selection modules in an end-to-end way,
achieving state-of-the-art performance on the public RSNA 2017 Pediatric Bone
Age benchmark dataset and outperforming other competitive methods by a
significant margin.
