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相关概念视频

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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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对医疗图像分类的敌对攻击

Min-Jen Tsai1, Ping-Yi Lin1, Ming-En Lee1

  • 1Institute of Information Management, National Yang Ming Chiao Tung University, Hsin-Chu 300, Taiwan.

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概括
此摘要是机器生成的。

医学图像分类容易受到像素攻击,影响计算机辅助诊断的准确性. 深度神经网络 (DNN) 模型努力抵御这些攻击,突出了对强大的诊断工具的需求.

关键词:
具有对抗性的学习.人工智能的人工智能是人工智能.计算机视觉 计算机视觉机器学习是机器学习.这是一种元启发式 (metaheuristic) 听证.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算机科学 计算机科学

背景情况:

  • 来自各种放射技术的医疗图像的数量越来越多.
  • 计算机辅助诊断 (CAD) 系统增强了临床应用.
  • 图像设施中的像素不准确性可能导致错误分类和错误的临床决策.

研究的目的:

  • 研究单像素和多像素对抗攻击对深度神经网络 (DNN) 模型的影响.
  • 评估像素操纵对分类性能和稳定性的影响.
  • 评估医疗图像分类对像素级攻击的脆弱性.

主要方法:

  • 对DNN模型进行了单像素和多像素级别的攻击.
  • 使用了常见的多类和多标签医疗图像数据集.
  • 进行了改变受影响像素数量的实验,以分析对DNN的影响.

主要成果:

  • 医疗图像显示了对像素攻击的显著脆弱性.
  • DNN模型的分类性能和稳定性受到像素操纵的负面影响.
  • 即使是微小的像素变化也会损害用于诊断目的的图像完整性.

结论:

  • 像素攻击对医学图像分类的准确性构成重大威胁.
  • 对于可靠的计算机辅助诊断,DNN模型对抗对抗攻击的稳定性至关重要.
  • 需要进一步的研究来开发医学成像AI的防御机制.