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.

本研究提出了基于课程学习的的策略来支持从 X 射线图像中分类识别近端股骨骨折，并将基于医学决策树和多个专家的注释不一致性等医学知识作为依据，使我们能够为每个训练样本分配一个难度等级。通过启动学习 “简单” 示例并向 “困难” 的示例移动，我们证明了该模型可以在更少的数据情况下达到更好的性能，与类均匀和随机策略相比，提出的基于医学知识的课程表现在准确性方面达到了高达 15％的优势，并实现了有经验的创伤外科医生的表现水平。