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GIONet:全球信息优化网络,通过COVID-GAN和域对抗策略实现多中心COVID-19诊断

Jing Zhang¹, Yiyao Liu², Baiying Lei²

¹Department of Radiology, The Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, 518000, China.

Computers in biology and medicine

|June 12, 2023

概括

一个新的AI模型从CT扫描中准确诊断COVID-19肺炎,优于现有方法. 这一进步对于区分COVID-19和社区获得性肺炎 (CAP) 以及处理各种数据源至关重要.

科学领域:

人工智能的人工智能
医疗成像医学成像
放射学放射学是一门学科.

背景情况:

使用CT扫描来区分COVID-19肺炎和社区获得性肺炎 (CAP) 具有挑战性,因为这些特征重叠.
现有的自动诊断方法在3类分类 (健康,CAP,COVID-19) 和多中心数据异质性方面扎.

研究的目的:

开发一种先进的AI模型,从CT扫描中准确地对COVID-19肺炎进行分类.
解决现有方法在处理不同医疗中心数据变化的局限性.

主要方法:

提出了一个全球信息优化网络 (GIONet),结合了3D CNN与图形增强的聚合和多尺度的自我注意力,以改进全球特征提取.
实施跨中心域对抗学习以减轻数据异质性,并使用生成对抗网络进行数据平衡.

主要成果:

在混合数据集上达到99.17%的高精度.
86.73%和89.61%的强烈的跨中心任务准确度,表明不同数据源的稳定性.

结论:

具有域对抗学习的GIONet模型有效地改善了从CT扫描中诊断COVID-19肺炎.
拟议的方法显示出在各种临床环境中可靠,自动化疾病分类的巨大潜力.

关键词:

诊断COVID-19的诊断结果是什么这就是COVID-GANAN.域对抗策略域对抗策略全球信息优化网络全球信息优化网络

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