  Current deep-learning based methods do not easily integrate to clinical
protocols, neither take full advantage of medical knowledge. In this work, we
propose and compare several strategies relying on curriculum learning, to
support the classification of proximal femur fracture from X-ray images, a
challenging problem as reflected by existing intra- and inter-expert
disagreement. Our strategies are derived from knowledge such as medical
decision trees and inconsistencies in the annotations of multiple experts,
which allows us to assign a degree of difficulty to each training sample. We
demonstrate that if we start learning "easy" examples and move towards "hard",
the model can reach a better performance, even with fewer data. The evaluation
is performed on the classification of a clinical dataset of about 1000 X-ray
images. Our results show that, compared to class-uniform and random strategies,
the proposed medical knowledge-based curriculum, performs up to 15% better in
terms of accuracy, achieving the performance of experienced trauma surgeons.
