最先进的贝叶斯深度学习和统计策略,通过胸部X射线成像来缓解COVID-19检测中的偏差
Yuanyuan Chen1, Waqas Khan2,3, Farman Ali4,5
1The 960th Hospital of the PLA Joint Logistics Support Force, Jinan, Shandong Province, China.
Scientific reports
|December 14, 2025
概括
这项研究引入了贝叶斯的深度学习框架,用于胸部X射线分析,在分类肺部疾病方面达到98.33%的准确性,包括COVID-19,对COVID-19检测有100%的灵敏度.
科学领域:
- 医疗成像中的人工智能
- 放射学和肺部医学 肺部医学
- 深度学习用于疾病分类.
背景情况:
- 胸部放射 (CXR) 对于肺部疾病管理至关重要,但由于偏见和领域转移,在COVID-19分类方面面临挑战.
- 使用CXRs检测COVID-19的现有方法容易受到各种数据缺陷的影响.
- 从CXR中准确而强大的COVID-19分类对于及时的患者分拣和护理至关重要.
研究的目的:
- 开发和评估一个多阶段的贝叶斯深度学习框架,用于从CXRs分类COVID-19,正常,病毒性肺炎和细菌性肺炎.
- 评估模型在评分COVID-19严重程度方面的能力.
- 量化拟议框架对各种图像退化造成的强度.
主要方法:
- 一个多阶段的贝叶斯深度学习框架,整合了肺部细分,细分引导分类,校准组合和不确定性估计.
- 对1531个CXR进行培训和测试,其中包括100张COVID-19图像.
- 通过压力测试进行稳定性评估,使用五种图像降解 (噪音,模糊,压缩,减样).
主要成果:
- 最终的组合模型实现了98.33%的测试准确性和100%的敏感性,用于检测COVID-19在测试的分裂.
- 在中度图像退化下,宏观AUC下降是最小的,但在严重模糊或降低样本时增加.
- 使用突出性和上下文相关性分析来识别模型预测中的潜在虚假线索.
结论:
- 拟议的贝叶斯深度学习框架显示了高准确度和敏感性,用于Covid-19的分类和严重程度分级.
- 该框架显示了对常见图像损坏的可量化的稳定性,尽管在严峻的条件下,性能可能会下降.
- 未来的工作包括对更大,多站点数据集的外部验证,如COVIDx和BIMCV-COVID19+,以进一步确认可通用性.
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