Abstract
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 this http URL 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 this http URL quantitatively demonstrate the effectiveness and feasibility of our relevance measure using a public dataset.
Abstract (translated)
内容基于图像检索（CBIR）在医学领域的性能评估仍然是一个关键但尚未解决的问题。文献中提出了多种解决方案的评价指标，其中大多数现有的度量标准（如精确度、召回率等）是从分类任务中借鉴而来的，并需要手动标签作为真实情况。然而，在特定的主题领域中，这样的标签往往难以获得且成本高昂。 医学图像通常与放射学报告相关联或在文献图例中标注描述性说明文本，这些文本包含可以用来评估CBIR的有用信息。研究人员指出，隐藏于这些文本中的医学概念可以用作评价CBIR的基础。然而，目前的工作常常将这些医学概念视为独立和孤立的标签，而实际上各种概念之间的细微关系被忽略了。 在本工作中，我们引入了知识图谱来衡量不同医学概念间的距离，并提出了一种新的相关性度量方法用于评估CBIR，通过定义两组医学概念之间基于近似匹配的相关得分，这使我们能够间接地量化医学图像之间的相似性。我们将使用公共数据集定量展示该相关性度量的有效性和可行性。
URL
https://arxiv.org/abs/2506.13509
