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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 …