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相关概念视频

Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
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Radiological Investigation I: X-ray and CT01:30

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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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X-ray Imaging01:24

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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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相关实验视频

Updated: Jul 2, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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探索深度COVID-19分类的解释性技术,使用胸部X射线图像进行分类.

Soumick Chatterjee1,2,3, Fatima Saad4,5, Chompunuch Sarasaen4,5,6

  • 1Data and Knowledge Engineering Group, Otto von Guericke University, 39106 Magdeburg, Germany.

Journal of imaging
|February 23, 2024
PubMed
概括

深度学习模型从胸部X射线中准确诊断了COVID-19,一个组合模型获得了F1得分0.89. 解释性分析显示ResNet模型为诊断决策提供了最清晰的见解.

关键词:
在 COVID-19 疫情中,胸部X射线 胸部X射线深度学习是一种深度学习.解释性分析 解释性分析模型组合 模型组合 模型组合多个标签的图像分类.肺炎是一种肺炎.

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科学领域:

  • 医学成像和人工智能 医学成像和人工智能
  • 医疗保健中的深度学习
  • 放射学和诊断成像 放射学和诊断成像

背景情况:

  • 随着COVID-19的流行,人们越来越需要快速,准确的诊断工具.
  • 医学成像,特别是胸部X射线,对于识别呼吸系统疾病至关重要.
  • 人工智能 (AI) 具有提高诊断准确性和效率的潜力.

研究的目的:

  • 用胸部X射线图像评估五种深度学习模型及其组合用于分类COVID-19,肺炎和健康受试者的性能.
  • 用各种本地和全球技术评估这些深度学习模型的可解释性.
  • 根据性能和可解释性来确定COVID-19诊断的最有效模型.

主要方法:

  • 使用了五种深度学习模型 (ResNet18,ResNet34,InceptionV3,InceptionResNetV2,DenseNet161) 来对胸部X射线图像进行多标签分类.
  • 采用集体方法,以多数投票结合来自单个模型的预测.
  • 应用了局部可解释性方法 (封闭,突出等). 和全局技术 (神经元激活档案) 来分析模型行为.

主要成果:

  • 合奏模型在COVID-19分类中获得了0.89的平均微型F1得分.
  • 对于COVID-19分类的个体模型性能从平均微F1得分0.66到0.875.5不等.
  • 定性分析表明,ResNet模型与其他评估网络相比,提供了更好的解释性.

结论:

  • 深度学习模型,特别是合奏,在通过胸部X射线诊断COVID-19方面表现出高效.
  • 模型的解释性对于在临床环境中理解和信任人工智能驱动的诊断工具至关重要.
  • 该研究强调了将可解释性方法整合到医疗AI应用的模型选择过程中的价值.