基于解剖结构的图像检索，提升罕见病识别准确率
CheXtriev: Anatomy-Centered Representation for Case-Based Retrieval of Chest Radiographs
- 用图注意力网络提取特定部位特征
- 检索准确率比现有方法高18%~26%
- 适合医学影像检索与辅助诊断场景
我们提出CheXtriev，一种基于图结构的解剖中心化胸部X光片检索框架。不同于以往聚焦全局特征的方法，该方法利用图变压器从特定解剖区域提取信息特征，并捕捉解剖位置与病灶之间的空间上下文关系及相互作用。这种基于循证解剖学的上下文建模，生成更丰富的解剖感知表示，显著提升检索准确性、有效性和效率，尤其在少见病灶检索上表现突出。CheXtriev在检索准确率上超越当前最优全局与局部方法18%至26%，在排名质量上提升11%至23%。代码已公开于https://github.com/cvit-mip/chextriev。
原文摘要 · Abstract (English)
We present CheXtriev, a graph-based, anatomy-aware framework for chest radiograph retrieval. Unlike prior methods focussed on global features, our method leverages graph transformers to extract informative features from specific anatomical regions. Furthermore, it captures spatial context and the interplay between anatomical location and findings. This contextualization, grounded in evidence-based anatomy, results in a richer anatomy-aware representation and leads to more accurate, effective and efficient retrieval, particularly for less prevalent findings. CheXtriv outperforms state-of-the-art global and local approaches by 18% to 26% in retrieval accuracy and 11% to 23% in ranking quality. The code is available at https://github.com/cvit-mip/chextriev.
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