CFJLNet: Coarse and Fine Feature Joint Learning Network for Bone Age Assessment

Effectively locating fine discriminative regions and fully analyzing fine region features is a challenging task in bone age assessment (BAA). Existing annotation-based methods are labor-intensive and attention-based methods can only learn semantic global features from coarse regions. In this article, in order to efficiently locate coarse and fine regions and fully learn features of them, we present a “coarse and fine feature joint learning network” (CFJLNet, including a coarse and a fine branch). Specifically, in each of the two branches of the CFJLNet, we first obtain attention used to locate discriminative regions and then design a “dual feature fusion” (DFF) module that can capture long-term dependencies in attention while fully fuse attention and features. Furthermore, for accurate localization of fine regions, we design an “Attention in Attention” (
$A^{2}$
) module to force coarse attention to guide the generation of fine attention. In particular, the visualization show that regions located by fine attention are consistent with the regions of interest (ROIs) used by TW3 method. We empirically analyze the contributions of the DFF and
$A^{2}$
modules in our network and demonstrate their superiority. The experimental results showed a mean absolute error (MAE) of 4.07 months on the RSNA dataset. To validate the generalizability of the proposed network, experiments are conducted on two face datasets, and it also achieves excellent performance.