  Elbow fracture diagnosis often requires patients to take both frontal and
lateral views of elbow X-ray radiographs. In this paper, we propose a multiview
deep learning method for an elbow fracture subtype classification task. Our
strategy leverages transfer learning by first training two single-view models,
one for frontal view and the other for lateral view, and then transferring the
weights to the corresponding layers in the proposed multiview network
architecture. Meanwhile, quantitative medical knowledge was integrated into the
training process through a curriculum learning framework, which enables the
model to first learn from "easier" samples and then transition to "harder"
samples to reach better performance. In addition, our multiview network can
work both in a dual-view setting and with a single view as input. We evaluate
our method through extensive experiments on a classification task of elbow
fracture with a dataset of 1,964 images. Results show that our method
outperforms two related methods on bone fracture study in multiple settings,
and our technique is able to boost the performance of the compared methods. The
code is available at https://github.com/ljaiverson/multiview-curriculum.
