College of Health Sciences, Center for Biomedical Data and Language Processing, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
ChengDe Petroleum College, Chengde, China.
J Med Syst. 2018 Apr 25;42(6):105. doi: 10.1007/s10916-018-0954-1.
Most current image retrieval methods require constructing semantic metadata for representing image content. To manually create semantic metadata for medical images is time-consuming, yet it is a crucial component for query expansion. We proposed a new method for searching medical image notes that uses semantic metadata to improve query expansion and leverages a knowledge model developed specifically for the medical image domain to create relevant metadata. We used a syntactic parser and the Unified Medical Language System to analyze the corpus and store text information as semantic metadata in a knowledge model. Our new method has an interactive interface that allows users to provide relevance feedback and construct new queries more efficiently. Sixteen medical professionals evaluated the query expansion module, and each evaluator had prior experience searching for medical images. When using the initial query as the baseline standard, expanded queries achieved a performance boost of 22.6% in terms of the relevance score on first ten results (P-value<0.05). When using Google as another baseline, our system performed 24.6% better in terms of relevance score on the first ten results (P-value<0.05). Overall, 75% of the evaluators said the semantic-enhanced query expansion workflow is logical, easy to follow, and comfortable to use. In addition, 62% of the evaluators preferred using our system instead of Google. Evaluators who were positive about our system found the knowledge map-based visualization of candidate medical search terms helpful in refining cases from the initial search results.
大多数当前的图像检索方法都需要构建语义元数据来表示图像内容。手动为医学图像创建语义元数据既耗时又费力，但它是查询扩展的关键组成部分。我们提出了一种新的搜索医学图像注释的方法，该方法使用语义元数据来改进查询扩展，并利用专门为医学图像领域开发的知识模型来创建相关的元数据。我们使用语法分析器和统一医学语言系统来分析语料库，并将文本信息存储为知识模型中的语义元数据。我们的新方法具有交互界面，允许用户提供相关性反馈并更有效地构建新查询。十六名医学专业人员评估了查询扩展模块，每位评估者都有搜索医学图像的经验。当使用初始查询作为基线标准时，扩展查询在前十条结果的相关性得分上提高了 22.6%（P 值<0.05）。当使用 Google 作为另一个基线时，我们的系统在前十条结果的相关性得分上提高了 24.6%（P 值<0.05）。总的来说，75%的评估者认为语义增强的查询扩展工作流程是合乎逻辑的、易于遵循的和舒适的。此外，62%的评估者更喜欢使用我们的系统而不是 Google。对我们的系统持肯定态度的评估者发现，基于知识图的候选医学搜索词可视化有助于从初始搜索结果中细化案例。
