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Training Strategy for Performance Improvement in Computer-Assisted Detection of Lesions: Based on Multi-institutional Study in Teleradiology Environment

机译:病灶计算机辅助检测中性能改进的培训策略:基于远程放射学环境中的多机构研究

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The performance of computer-assisted detection (CAD) software depends on quality and quantity of the dataset used for supervised learning. If the data characteristics in development and practical use are different, the performance of CAD software will be degraded. Therefore, it is necessary to continuously collect data for supervised learning in practical use, and to improve CAD software by retraining with the collected data. For this purpose, we developed a web-based CAD software processing and evaluation platform (CIRCUS CS), which provides on-line processing of CAD software and interfaces to evaluate the results obtained from CAD software. For a multi-institutional study, we implemented CIRCUS CS into a teleradiology environment, which has been in practical use since September 2011. In this study, we investigated the performance improvement of CAD software for each institution based on retraining through a simulation-based study. According to the results, the performance of CAD software for each institution was improved by retraining.
机译:计算机辅助检测(CAD)软件的性能取决于用于监督学习的数据集的质量和数量。如果开发和实际使用中的数据特征不同,则CAD软件的性能将降低。因此,有必要在实际使用中连续收集数据以进行监督学习,并通过对所收集的数据进行再培训来改进CAD软件。为此,我们开发了基于Web的CAD软件处理和评估平台(CIRCUS CS),该平台提供了CAD软件的在线处理和界面,以评估从CAD软件获得的结果。对于多机构研究,我们将CIRCUS CS实施到自2011年9月起已投入实际使用的远程放射学环境中。在这项研究中,我们通过基于模拟的研究进行再培训,研究了每个机构的CAD软件的性能改进。根据结果​​,通过再培训提高了每个机构的CAD软件的性能。

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