肿瘤影像档案中非影像资料的本体增强表示。

CEUR workshop proceedings Pub Date : 2018-08-01
Jonathan P Bona, Tracy S Nolan, Mathias Brochhausen
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引用次数: 0

摘要

癌症影像档案(TCIA)拥有超过1100万张与癌症相关的去识别医学图像,用于研究再利用。它们是围绕dicom格式的放射学集合进行组织的,这些集合按疾病类型、模式或研究重点分组。许多集合还包括各种格式的各种非图像数据集,而没有一种通用的方法来表示数据所涉及的实体。本文描述了通过使用开放生物医学本体将这些不同的非图像数据转换为集成语义表示,使它们更易于访问和使用的工作,突出了数据中遇到的障碍,并展示了在选定集合中发现的详细表示数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ontology-Enhanced Representations of Non-image Data in The Cancer Imaging Archive.

The Cancer Imaging Archive (TCIA) hosts over 11 million de-identified medical images related to cancer for research reuse. These are organized around DICOM-format radiological collections that are grouped by disease type, modality, or research focus. Many collections also include diverse non-image datasets in a variety of formats without a common approach to representing the entities that the data are about. This paper describes work to make these diverse non-image data more accessible and usable by transforming them into integrated semantic representations using Open Biomedical Ontologies, highlights obstacles encountered in the data, and presents detailed representations data found in select collections.

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