Department of Information Technology, University of Huelva, Huelva, Spain.
J Am Med Inform Assoc. 2013 Nov-Dec;20(6):1014-20. doi: 10.1136/amiajnl-2012-000943. Epub 2012 Sep 5.
We explored two strategies for query expansion utilizing medical subject headings (MeSH) ontology to improve the effectiveness of medical image retrieval systems. In order to achieve greater effectiveness in the expansion, the search text was analyzed to identify which terms were most amenable to being expanded.
To perform the expansions we utilized the hierarchical structure by which the MeSH descriptors are organized. Two strategies for selecting the terms to be expanded in each query were studied. The first consisted of identifying the medical concepts using the unified medical language system metathesaurus. In the second strategy the text of the query was divided into n-grams, resulting in sequences corresponding to MeSH descriptors.
For the evaluation of the system, we used the collection made available by the ImageCLEF organization in its 2011 medical image retrieval task. The main measure of efficiency employed for evaluating the techniques developed was the mean average precision (MAP).
Both strategies exceeded the average MAP score in the ImageCLEF 2011 competition (0.1644). The n-gram expansion strategy achieved a MAP of 0.2004, which represents an improvement of 21.89% over the average MAP score in the competition. On the other hand, the medical concepts expansion strategy scored 0.2172 in the MAP, representing a 32.11% improvement. This run won the text-based medical image retrieval task in 2011.
Query expansion exploiting the hierarchical structure of the MeSH descriptors achieved a significant improvement in image retrieval systems.
我们探索了两种利用医学主题词（MeSH）本体进行查询扩展的策略，以提高医学图像检索系统的有效性。为了实现更大的扩展效果，分析了搜索文本以确定哪些术语最适合扩展。
为了进行扩展，我们利用了 MeSH 描述符的层次结构进行组织。研究了两种在每个查询中选择要扩展的术语的策略。第一种策略是使用统一医学语言系统术语集来识别医学概念。第二种策略是将查询的文本分为 n 元组，生成与 MeSH 描述符相对应的序列。
为了评估系统，我们使用了 ImageCLEF 组织在其 2011 年医学图像检索任务中提供的集合。用于评估所开发技术的主要效率衡量标准是平均平均精度（MAP）。
这两种策略都超过了 ImageCLEF 2011 竞赛的平均 MAP 得分（0.1644）。n 元组扩展策略的 MAP 达到 0.2004，比竞赛中的平均 MAP 得分提高了 21.89%。另一方面，医学概念扩展策略在 MAP 中得分为 0.2172，代表提高了 32.11%。该运行在 2011 年赢得了基于文本的医学图像检索任务。
利用 MeSH 描述符的层次结构进行查询扩展，显著提高了图像检索系统的性能。
