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Analyzing Web Log Files of the Health On the Net HONmedia Search Engine to Define Typical Image Search Tasks for Image Retrieval Evaluation

机译:在HONmedia网络搜索引擎上分析运行状况的Web日志文件,以定义用于图像检索评估的典型图像搜索任务

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摘要

Medical institutions produce ever-increasing amount of diverse information. The digital form makes these data available for the use on more than a single patient. Images are no exception to this. However, less is known about how medical professionals search for visual medical information and how they want to use it outside of the context of a single patient. This article analyzes ten months of usage log files of the Health on the Net (HON) medical media search engine. Key words were extracted from all queries and the most frequent terms and subjects were identified. The dataset required much pre-treatment. Problems included national character sets, spelling errors and the use of terms in several languages.rnThe results show that media search, particularly for images, was frequently used. The most common queries were for general concepts (e.g., heart, lung). To define realistic information needs for the ImageCLEFmed challenge evaluation (Cross Language Evaluation Forum medical image retrieval), we used frequent queries that were still specific enough to at least cover two of the three axes on modality, anatomic region, and pathology. Several research groups evaluated their image retrieval algorithms based on these defined topics.
机译:医疗机构产生越来越多的各种信息。数字形式使这些数据可供多个患者使用。图像也不例外。但是,对于医疗专业人员如何搜索可视医疗信息以及他们如何在单个患者的环境之外使用它的信息知之甚少。本文分析了“健康在线”(HON)医疗媒体搜索引擎的十个月使用日志文件。从所有查询中提取关键词,并确定最常用的术语和主题。该数据集需要大量预处理。问题包括国家字符集,拼写错误和多种语言中的术语使用。结果表明,经常使用媒体搜索,尤其是图像搜索。最常见的查询是关于一般概念的查询(例如,心脏,肺脏)。为了定义ImageCLEFmed挑战评估(跨语言评估论坛医学图像检索)的现实信息需求,我们使用了仍然足够具体的频繁查询,至少涵盖了模态,解剖区域和病理学这三个轴中的两个。几个研究小组基于这些定义的主题评估了他们的图像检索算法。

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