  Bone age assessment (BAA) is clinically important as it can be used to
diagnose endocrine and metabolic disorders during child development. Existing
deep learning based methods for classifying bone age use the global image as
input, or exploit local information by annotating extra bounding boxes or key
points. However, training with the global image underutilizes discriminative
local information, while providing extra annotations is expensive and
subjective. In this paper, we propose an attention-guided approach to
automatically localize the discriminative regions for BAA without any extra
annotations. Specifically, we first train a classification model to learn the
attention maps of the discriminative regions, finding the hand region, the most
discriminative region (the carpal bones), and the next most discriminative
region (the metacarpal bones). Guided by those attention maps, we then crop the
informative local regions from the original image and aggregate different
regions for BAA. Instead of taking BAA as a general regression task, which is
suboptimal due to the label ambiguity problem in the age label space, we
propose using joint age distribution learning and expectation regression, which
makes use of the ordinal relationship among hand images with different
individual ages and leads to more robust age estimation. Extensive experiments
are conducted on the RSNA pediatric bone age data set. Using no training
annotations, our method achieves competitive results compared with existing
state-of-the-art semi-automatic deep learning-based methods that require manual
annotation. Code is available at https:
//github.com/chenchao666/Bone-Age-Assessment.
