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.
CITATION STYLE
Husseini, M., Sekuboyina, A., Loeffler, M., Navarro, F., Menze, B. H., & Kirschke, J. S. (2020). Grading Loss: A Fracture Grade-Based Metric Loss for Vertebral Fracture Detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12266 LNCS, pp. 733–742). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59725-2_71
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