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A medical ontology for intelligent web-based skin lesions image retrieval.

机译:用于基于Web的智能皮肤病变图像检索的医学本体。

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摘要

Researchers have applied increasing efforts towards providing formal computational frameworks to consolidate the plethora of concepts and relations used in the medical domain. In the domain of skin related diseases, the variability of semantic features contained within digital skin images is a major barrier to the medical understanding of the symptoms and development of early skin cancers. The desideratum of making these standards machine-readable has led to their formalization in ontologies. In this work, in an attempt to enhance an existing Core Ontology for skin lesion images, hand-coded from image features, high quality images were analyzed by an autonomous ontology creation engine. We show that by exploiting agglomerative clustering methods with distance criteria upon the existing ontological structure, the original domain model could be enhanced with new instances, attributes and even relations, thus allowing for better classification and retrieval of skin lesion categories from the web.
机译:研究人员已投入越来越多的精力来提供正式的计算框架,以巩固医学领域中使用的大量概念和关系。在皮肤相关疾病领域,数字皮肤图像中包含的语义特征的可变性是医学上了解早期皮肤癌的症状和发展的主要障碍。使这些标准可机读的迫切需求导致了它们在本体中的形式化。在这项工作中,为了增强现有的皮肤病变图像核心本体(通过图像特征进行手工编码),通过自主本体创建引擎对高质量图像进行了分析。我们表明,通过在现有本体结构上使用具有距离标准的聚集聚类方法,可以通过新实例,属性甚至关系来增强原始域模型,从而可以更好地从网络上分类和检索皮肤病变类别。

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