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Osteoporotic vertebral fractures have a severe impact on patients' overallwell-being but are severely under-diagnosed. These fractures present themselvesat various levels of severity measured using the Genant's grading scale.Insufficient annotated datasets, severe data-imbalance, and minor difference inappearances between fractured and healthy vertebrae make naive classificationapproaches result in poor discriminatory performance. Addressing this, wepropose a representation learning-inspired approach for automated vertebralfracture detection, aimed at learning latent representations efficient forfracture detection. Building on state-of-art metric losses, we present a novelGrading Loss for learning representations that respect Genant's fracturegrading scheme. On a publicly available spine dataset, the proposed lossfunction achieves a fracture detection F1 score of 81.5%, a 10% increase over anaive classification baseline.

Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection