Dokuz Eylul University, Department of Computer Engineering, The Graduate School of Natural and Applied Science, Izmir, Turkey.
Comput Biol Med. 2013 May;43(4):301-11. doi: 10.1016/j.compbiomed.2013.01.001. Epub 2013 Feb 14.
This paper presents an ontology-based annotation system and BI-RADS (Breast Imaging Reporting and Data System) score reasoning with Semantic Web technologies in mammography. The annotation system is based on the Mammography Annotation Ontology (MAO) where the BI-RADS score reasoning works. However, ontologies are based on crisp logic and they cannot handle uncertainty. Consequently, we propose a Bayesian-based approach to model uncertainty in mammography ontology and make reasoning possible using BI-RADS scores with SQWRL (Semantic Query-enhanced Web Rule Language). First, we give general information about our system and present details of mammography annotation ontology, its main concepts and relationships. Then, we express uncertainty in mammography and present approaches to handle uncertainty issues. System is evaluated with a manually annotated dataset DEMS (Dokuz Eylul University Mammography Set) and DDSM (Digital Database for Screening Mammography). We give the result of experimentations in terms of accuracy, sensitivity, precision and uncertainty level measures.
本文提出了一种基于本体的标注系统和 BI-RADS（乳腺成像报告和数据系统）评分推理，使用语义 Web 技术在乳腺 X 线摄影中。该标注系统基于乳腺 X 线摄影标注本体（MAO），BI-RADS 评分推理在此本体上进行。然而，本体基于清晰逻辑，无法处理不确定性。因此，我们提出了一种基于贝叶斯的方法来对乳腺 X 线摄影本体中的不确定性进行建模，并使用 BI-RADS 评分和 SQWRL（语义查询增强 Web 规则语言）进行推理。首先，我们给出了系统的一般信息，并介绍了乳腺 X 线摄影标注本体、其主要概念和关系的详细信息。然后，我们表达了乳腺 X 线摄影中的不确定性，并提出了处理不确定性问题的方法。系统使用手动标注数据集 DEMS（多所埃于吕大学乳腺 X 线摄影集）和 DDSM（数字筛查乳腺数据库）进行评估。我们给出了基于准确性、敏感性、精度和不确定性水平度量的实验结果。
