Osteoporotic vertebral fractures have a severe impact on patients' overall
well-being but are severely under-diagnosed. These fractures present themselves
at various levels of severity measured using the Genant's grading scale.
Insufficient annotated datasets, severe data-imbalance, and minor difference in
appearances between fractured and healthy vertebrae make naive classification
approaches result in poor discriminatory performance. Addressing this, we
propose a representation learning-inspired approach for automated vertebral
fracture detection, aimed at learning latent representations efficient for
fracture detection. Building on state-of-art metric losses, we present a novel
Grading Loss for learning representations that respect Genant's fracture
grading scheme. On a publicly available spine dataset, the proposed loss
function achieves a fracture detection F1 score of 81.5%, a 10% increase over a
naive classification baseline.Comment: To be presented at MICCAI 202