用知识图谱衡量医学概念关联，提升图像检索评估精度
A Semantically-Aware Relevance Measure for Content-Based Medical Image Retrieval Evaluation
- 基于知识图谱计算医学概念间语义距离，构建匹配评分
- 在公开数据集上验证新度量方法有效且可行
- 适合医学图像检索评估与临床辅助诊断研究者
内容基医学图像检索（CBIR）的性能评估仍是当前重要但未解决的问题，尤其在医学领域。现有评估指标（如精确率、召回率）多源自分类任务，依赖人工标注作为真实标签，但在特定主题领域中此类标注成本高且难以获取。医学图像常配有放射学报告或文献图注，其中包含可用于评估CBIR的语义信息。已有研究认为这些医学概念可作为评估基础，但通常将其视为孤立标签，忽视了概念间的细微关联。本文引入知识图谱来衡量医学概念间的语义距离，提出一种基于近似匹配的新的相关性度量方法，通过两个医学概念集合间的得分间接评估医学图像相似性。我们在公开数据集上定量验证了该方法的有效性和可行性。
原文摘要 · Abstract (English)
Performance evaluation for Content-Based Image Retrieval (CBIR) remains a crucial but unsolved problem today especially in the medical domain. Various evaluation metrics have been discussed in the literature to solve this problem. Most of the existing metrics (e.g., precision, recall) are adapted from classification tasks which require manual labels as ground truth. However, such labels are often expensive and unavailable in specific thematic domains. Furthermore, medical images are usually associated with (radiological) case reports or annotated with descriptive captions in literature figures, such text contains information that can help to assess CBIR.Several researchers have argued that the medical concepts hidden in the text can serve as the basis for CBIR evaluation purpose. However, these works often consider these medical concepts as independent and isolated labels while in fact the subtle relationships between various concepts are neglected. In this work, we introduce the use of knowledge graphs to measure the distance between various medical concepts and propose a novel relevance measure for the evaluation of CBIR by defining an approximate matching-based relevance score between two sets of medical concepts which allows us to indirectly measure the similarity between medical images.We quantitatively demonstrate the effectiveness and feasibility of our relevance measure using a public dataset.
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