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Intelligent generator of big data medical imaging repositories

机译：大数据医学影像存储库的智能生成器

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摘要

The production of medical imaging data has grown tremendously in the last decades. Nowadays, even small institutions produce a considerable amount of studies. Furthermore, the general trend in new imaging modalities is to produce more data per examination. As a result, the design and implementation of tomorrow's storage and communication systems must deal with big data issues. The research on technologies to cope with big data issues in large scale medical imaging environments is still in its early stages. This is mostly due to the difficulty of implementing and validating new technological approaches in real environments, without interfering with clinical practice. Therefore, it is crucial to create test bed environments for research purposes. This study proposes a methodology for creating simulated medical imaging repositories, based on the indexing of model datasets, extraction of patterns and modelling of study production. The system creates a model from a real-world repository's representative time window and expands it according to on-going research needs. In addition, the solution provides distinct approaches to reducing the size of the generated datasets. The proposed system has already been used by other research projects in validation processes that aim to assess the performance and scalability of developed systems.
机译：在过去的几十年中，医学成像数据的生产有了巨大的增长。如今，即使是小型机构也要进行大量的研究。此外，新的成像方式的总体趋势是每次检查产生更多的数据。结果，未来存储和通信系统的设计和实现必须处理大数据问题。应对大规模医学成像环境中的大数据问题的技术的研究仍处于早期阶段。这主要是由于在不干扰临床实践的情况下难以在实际环境中实施和验证新技术方法的缘故。因此，为研究目的创建测试平台环境至关重要。这项研究提出了一种基于模型数据集的索引，模式的提取和研究成果的建模方法来创建模拟医学影像库的方法。该系统从现实世界存储库的代表性时间窗口创建模型，并根据持续的研究需求对其进行扩展。另外，该解决方案提供了减少生成数据集大小的独特方法。提议的系统已被其他研究项目用于验证过程，旨在评估已开发系统的性能和可伸缩性。

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