胸部X射线图像中的肺肺炎严重程度评分使用变压器
Bouthaina Slika1,2,3, Fadi Dornaika4,5, Hamid Merdji6,7
1University of the Basque Country UPV/EHU, San Sebastian, Spain.
Medical & biological engineering & computing
|April 8, 2024
概括
这项研究介绍了ViTReg-IP,这是一种新的视觉转换器模型,用于使用胸部X射线有效评估COVID-19和肺炎的严重程度. 该模型在适度的计算需求下实现了高精度,有助于快速临床诊断.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 通过胸部X射线 (CXR) 准确的肺肺炎诊断和严重程度评估需要强大的方法.
- 当前的严重程度量化技术往往需要资源密集的培训.
- 需要高效的计算工具来快速识别和预测COVID-19的严重程度.
研究的目的:
- 开发一种高效,可适应的方法来诊断肺肺炎,并使用CXRs评估其严重程度.
- 引入一种具有视觉变压器 (ViT) 架构的新型神经网络模型,用于COVID-19和肺部疾病严重程度的量化.
- 评估模型在各种CXR数据集中的通用性.
主要方法:
- 开发了一种新的图像增强方案和视觉转换器回归器感染预测 (ViTReg-IP) 模型.
- 使用ViT架构与回归头进行严重性量化.
- 在多个开源胸部X-ray数据集上评估模型性能,并将其与现有的深度学习方法进行比较.
主要成果:
- 在地理范围和肺不透明度分别达到0.569和0.512的最小平均绝对误差 (MAE).
- 在地理范围和肺不透明度得分方面,达到0.923和0.855的最大皮尔森相关系数 (PC).
- 在严重程度量化方面表现出卓越的性能,具有强大的概括性和适度的计算要求.
结论:
- ViTReg-IP模型为COVID-19和肺部疾病严重程度评估提供了高效和准确的解决方案.
- 该模型在不同的数据集中表现出强大的概括性,使其适应临床使用.
- 公共可用的源代码有助于进一步研究和应用开发的方法.
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