  Elbow fractures are one of the most common fracture types. Diagnoses on elbow
fractures often need the help of radiographic imaging to be read and analyzed
by a specialized radiologist with years of training. Thanks to the recent
advances of deep learning, a model that can classify and detect different types
of bone fractures needs only hours of training and has shown promising results.
However, most existing deep learning models are purely data-driven, lacking
incorporation of known domain knowledge from human experts. In this work, we
propose a novel deep learning method to diagnose elbow fracture from elbow
X-ray images by integrating domain-specific medical knowledge into a curriculum
learning framework. In our method, the training data are permutated by sampling
without replacement at the beginning of each training epoch. The sampling
probability of each training sample is guided by a scoring criterion
constructed based on clinically known knowledge from human experts, where the
scoring indicates the diagnosis difficultness of different elbow fracture
subtypes. We also propose an algorithm that updates the sampling probabilities
at each epoch, which is applicable to other sampling-based curriculum learning
frameworks. We design an experiment with 1865 elbow X-ray images for a
fracture/normal binary classification task and compare our proposed method to a
baseline method and a previous method using multiple metrics. Our results show
that the proposed method achieves the highest classification performance. Also,
our proposed probability update algorithm boosts the performance of the
previous method.
