论文标题
评分损失：椎骨骨折检测的基于断裂等级的度量损失
Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection
论文作者
论文摘要
骨质疏松性椎骨骨折对患者的整体健康状况有严重影响，但严重诊断。这些裂缝以使用Genant的分级量表来测量的各种严重程度。注释数据集不足，严重的数据不平衡以及骨折和健康椎骨之间出现的较小差异使得幼稚的分类方法导致歧视性能差。在解决这个问题时，我们提出了一种以自动椎骨检测为代表学习启发的方法，旨在学习有效的裂缝检测潜在表示。在最先进的度量损失的基础上，我们为尊重Genant裂缝分级方案的学习表征提供了新的分级损失。在公开可用的脊柱数据集中，提议的损失功能达到了骨折检测F1分数为81.5％，比天真分类基线增长了10％。
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.
