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自我注意力驱动的提取胸部CT图像用于COVID-19评估

Victoria Fili¹, Michalis Savelonas¹

¹Department of Computer Science and Biomedical Informatics, University of Thessaly, Papasiopoulou 2-4, Lamia, 35131, Greece.

Biomedical physics & engineering express

|January 15, 2024

概括

这项研究引入了一种新的自我注意方法,用于使用胸部CT扫描进行COVID-19诊断. 该方法通过关注相关的图像区域来提高计算机辅助诊断 (CAD) 的透明度和准确性.

科学领域:

放射学和医学成像学医学成像学
人工智能在医学中的应用
计算病理学计算病理学

背景情况:

在COVID-19计算机辅助诊断 (CAD) 中的深度神经网络缺乏透明度,充当"黑子",并在所有图像区域均地影响结果.
医疗社区的信任受到现有的深度学习模型的不透明性阻碍,用于从胸部CT图像中检测COVID-19.

研究的目的:

开发一种新的,以自我注意力驱动的方法,用于基于内容的图像检索 (CBIR) 胸部CT图像用于COVID-19诊断.
通过结合视觉解释和专注于突出的图像区域来提高CAD系统的可解释性和可信度.

主要方法:

使用一种自我注意力机制,以梯度加权类激活映射 (Grad-CAM) 重量为指导,分析胸部CT图像.
由Grad-CAM生成的热图突出显示了肺组织和病变对COVID-19病理学的贡献,使用权重来加强检索过程中关键区域的影响.
该方法执行基于内容的图像检索 (CBIR),返回分类结果和类似图像的排名列表.

主要成果:

在使用DenseNet-201的COVID-CT数据集上,二进制分类准确率达到81.3%,在使用DenseNet-201的SARS-CoV-2数据集上达到96.4%.
两个数据集的假负率保持在3%以下.
根据Grad-CAM指导的CBIR框架,在近邻和前四个指标上,与普通CBIR相比,业绩略有改善.

结论:

拟议的自我注意力驱动方法提供了一个更透明和潜在的可靠的计算工具,用于COVID-19诊断,使用胸部CT扫描.
整合Grad-CAM和自我注意机制提高了用于医学诊断的图像检索性能.
这种方法可以改善医疗界对COVID-19检测的决策过程.

关键词:

在这里,我们可以看到CAD,CAD,CAD.印度央行 (CBIR) 的.在 COVID-19 疫情中,深度学习是一种深度学习.自己注意力自我注意力

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