基于双曲嵌入的细粒度肋骨骨折诊断：详细标注框架与多标签分类模型
Fine-Grained Rib Fracture Diagnosis with Hyperbolic Embeddings: A Detailed Annotation Framework and Multi-Label Classification Model
AI总结：
针对现有肋骨骨折数据集缺乏细粒度标注的问题，提出新型标注方案与基于双曲嵌入的跨模态分类模型，在AirRib和RibFrac数据集上均取得更优的分类效果。
摘要：
准确的肋骨骨折识别与分类对治疗规划至关重要。然而现有数据集往往缺乏细粒度标注，尤其是在肋骨骨折的特征、类型以及单根肋骨上的精确解剖位置方面。为解决这一问题，我们提出了一种专为骨折分类定制的新型肋骨骨折标注方案。此外，我们通过利用衔接放射影像与临床描述的跨模态嵌入来优化骨折分类。我们的方法采用双曲嵌入捕捉骨折的层级特性，将视觉特征与文本描述映射到共享的非欧流形中。该框架能够实现影像特征与临床描述之间更精细的相似度计算，兼顾骨折分类体系中固有的层级关系。实验结果表明，我们的方法在多个分类任务上优于现有方法，在AirRib数据集上平均召回率提升6%，在公开RibFrac数据集上提升17.5%。
英文摘要：
Accurate rib fracture identification and classification are essential for treatment planning. However, existing datasets often lack fine-grained annotations, particularly regarding rib fracture characterization, type, and precise anatomical location on individual ribs. To address this, we introduce a novel rib fracture annotation protocol tailored for fracture classification. Further, we enhance fracture classification by leveraging cross-modal embeddings that bridge radiological images and clinical descriptions. Our approach employs hyperbolic embeddings to capture the hierarchical nature of fracture, mapping visual features and textual descriptions into a shared non-Euclidean manifold. This framework enables more nuanced similarity computations between imaging characteristics and clinical descriptions, accounting for the inherent hierarchical relationships in fracture taxonomy. Experimental results demonstrate that our approach outperforms existing methods across multiple classification tasks, with average recall improvements of 6% on the AirRib dataset and 17.5% on the public RibFrac dataset.
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