用双模嵌入提升肋骨骨折精细分类，精准定位更准确。
Fine-Grained Rib Fracture Diagnosis with Hyperbolic Embeddings: A Detailed Annotation Framework and Multi-Label Classification Model
- 构建细粒度标注规范，融合影像与临床描述
- 在AirRib和RibFrac数据集上平均召回率分别提升6%和17.5%
- 适合医学影像分析、放射科辅助诊断研究者
准确识别和分类肋骨骨折对治疗规划至关重要。然而，现有数据集常缺乏细粒度标注，尤其在骨折特征、类型及单根肋骨精确解剖位置方面。为此，我们提出一种专为骨折分类设计的新型肋骨骨折标注协议，并通过跨模态嵌入增强分类性能，将影像与临床描述映射到非欧几里得流形中。该方法利用双曲嵌入捕捉骨折的层次结构，实现影像特征与临床描述间更精细的相似性计算，充分考虑骨折分类体系中的层级关系。实验表明，本方法在多个分类任务中优于现有技术，在AirRib数据集上平均召回率提升6%，在公开的RibFrac数据集上提升17.5%。
原文摘要 · Abstract (English)
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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