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

• URL: http://arxiv.org/abs/2008.07831v1
• Date: Tue, 18 Aug 2020 10:03:45 GMT
• Title: Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection
• Authors: Malek Husseini, Anjany Sekuboyina, Maximilian Loeffler, Fernando Navarro, Bjoern H. Menze, Jan S. Kirschke
• Abstract summary: We propose a representation learning-inspired approach for automated vertebral fracture detection. 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%.
• Score: 58.984536305767996
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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