一个可解释的人工智能模型,用于从胸部x射线图像中识别局部指标和检测肺部疾病

Shiva prasad Koyyada , Thipendra P. Singh
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引用次数: 2

摘要

放射科医生的主要职责之一是利用胸部x光片诊断肺部疾病。放射科医生在影像学上检查斑片状感染,并根据他们的知识做出理性的决定。卷积神经网络在从医学图像中分类和识别疾病方面表现得非常好。尽管深度学习(DL)模型是一种很有前途的预测技术,其精度相当于人,但通常缺乏可解释性,这是在高度监管的医疗保健行业中临床部署DL模型的关键组成部分。在本文中,我们模仿放射科医生的决策过程,通过每周监督学习和推导规则来识别胸部x射线图像的局部区分区域,并解释为什么DL方法会给出这样的结果。这个过程分三个阶段进行。第一阶段是训练一个分类问题的模型来预测肺部疾病。第二阶段是识别关键区域,并在识别出的具有关键区域的图像上训练模型。第三阶段结合当地和全球特征,学习更多的模式来分类疾病。局部和融合模型显示出显著的改进,在更少的时间内获得99.6%的精度。
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An explainable artificial intelligence model for identifying local indicators and detecting lung disease from chest X-ray images
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来源期刊
Healthcare analytics (New York, N.Y.)
Healthcare analytics (New York, N.Y.) Applied Mathematics, Modelling and Simulation, Nursing and Health Professions (General)
CiteScore
4.40
自引率
0.00%
发文量
0
审稿时长
79 days
期刊最新文献
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