将骨折形态分类从全局多标签转为局部多分类，提升诊断准确率。
Fracture Morphology Classification: Local Multiclass Modeling for Multilabel Complexity
- 用局部多分类替代全局多标签，简化复杂诊断任务。
- 在公开数据集上平均F1得分提升7.89%。
- 适合医学影像分析与儿童骨折辅助诊断研究者。
15%至45%的儿童在成长期间会经历骨折，准确诊断至关重要。骨折形态、位置及断端角度是关键诊断特征。本文提出一种方法，通过为骨折边界框自动分配全球AO编码，实现骨折形态的自动提取。该方法使公共数据集可被利用，并将全局多标签任务重构为局部多分类任务，在平均F1分数上提升了7.89%。然而，当使用不完善的骨折检测器时，性能下降，凸显真实场景部署的挑战。代码已开源。
原文摘要 · Abstract (English)
Between $15\,\%$ and $45\,\%$ of children experience a fracture during their growth years, making accurate diagnosis essential. Fracture morphology, alongside location and fragment angle, is a key diagnostic feature. In this work, we propose a method to extract fracture morphology by assigning automatically global AO codes to corresponding fracture bounding boxes. This approach enables the use of public datasets and reformulates the global multilabel task into a local multiclass one, improving the average F1 score by $7.89\,\%$. However, performance declines when using imperfect fracture detectors, highlighting challenges for real-world deployment. Our code is available on GitHub.
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