Triplet-Branch Network with Prior-Knowledge Embedding for Fatigue Fracture Grading

In recent years, there has been increasing awareness of the occurrence of fatigue fractures. Athletes and soldiers, who engaged in unaccustomed, repetitive or vigorous activities, are potential victims of such a fracture. Due to the slow-growing process of fatigue fracture, the early detection can effectively protect athletes and soldiers from the material bone breakage, which may result in the catastrophe of career retirement. In this paper, we propose a triplet-branch network (TBN) for the accurate fatigue fracture grading, which enables physicians to promptly take appropriate treatments. Particularly, the proposed TBN consists of three branches for representation learning, classifier learning and grade-related prior-knowledge learning, respectively. The former two branches are responsible to tackle the problem of class-imbalanced training data, while the latter one is implemented to embed grade-related prior-knowledge into the framework via an auxiliary ranking task. Extensive experiments have been conducted on our fatigue fracture X-ray image dataset. The experimental results show that our TBN can effectively address the problem of class-imbalanced training samples and achieve a satisfactory accuracy for fatigue fracture grading.