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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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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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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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基于深度学习的对象检测策略,用于在胸部X射线图像中检测和定位疾病.

Yi-Ching Cheng1, Yi-Chieh Hung1, Guan-Hua Huang1

  • 1Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan.

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深度学习通过自动检测异常区域来改善胸部X射线分析. 卷积神经网络 (CNN) 的性能优于变压器,提高了呼吸道和心血管疾病的诊断准确性和效率.

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科学领域:

  • 医学成像分析 医学成像分析
  • 医疗保健中的人工智能
  • 放射学 放射学是一门学科.

背景情况:

  • 胸部X射线 (CXR) 解释对于诊断呼吸道和心血管疾病至关重要.
  • 手动CXR分析面临的挑战包括主观性,时间消耗和诊断错误.
  • 需要自动化方法来提高CXR解释的准确性和一致性.

研究的目的:

  • 开发和评估基于深度学习的对象检测方法,用于自动识别和注释CXR图像中的异常.
  • 评估背景图像比例对模型性能的影响.
  • 为了比较卷积神经网络 (CNN) 和基于变压器的模型用于医疗图像分析.

主要方法:

  • 利用来自E-Da医院的疾病标记的CXR图像与边界框进行模型开发和测试.
  • 调查了各种训练数据集和方法来管理正常图像的流行.
  • 探索了一些射击物体检测技术,以解决特定疾病的有限数据.
  • 对比了CNN和变压器架构的性能.

主要成果:

  • 背景图像的比例显著影响了模型推理.
  • 整合二进制分类方案始终提高了模型性能.
  • 基于CNN的模型在所有测试的场景中,与基于变压器的模型相比,表现优越.

结论:

  • 开发了一种更有效,更可靠的系统,用于自动检测CXR图像中的疾病标签和界限框.
  • 深度学习,特别是CNN,为增强CXR分析提供了一个有前途的方法.
  • 该研究提供了对优化医疗图像分析的深度学习模型的见解,考虑数据不平衡和架构选择.