  Computerized automatic methods have been employed to boost the productivity
as well as objectiveness of hand bone age assessment. These approaches make
predictions according to the whole X-ray images, which include other objects
that may introduce distractions. Instead, our framework is inspired by the
clinical workflow (Tanner-Whitehouse) of hand bone age assessment, which
focuses on the key components of the hand. The proposed framework is composed
of two components: a Mask R-CNN subnet of pixelwise hand segmentation and a
residual attention network for hand bone age assessment. The Mask R-CNN subnet
segments the hands from X-ray images to avoid the distractions of other objects
(e.g., X-ray tags). The hierarchical attention components of the residual
attention subnet force our network to focus on the key components of the X-ray
images and generate the final predictions as well as the associated visual
supports, which is similar to the assessment procedure of clinicians. We
evaluate the performance of the proposed pipeline on the RSNA pediatric bone
age dataset and the results demonstrate its superiority over the previous
methods.
