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相关概念视频

X-ray Imaging01:24

X-ray Imaging

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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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Transverse-plane descriptors and modifiers of the SRS-Lenke-Aubin 3D classification provide complementary and clinically informative 3D features beyond 2D assessment in Lenke 1 AIS curves.

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相关实验视频

Updated: Jul 8, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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使用物体检测深度学习算法匿名化放射图.

Bardia Khosravi1, John P Mickley1, Pouria Rouzrokh1

  • 1From the Orthopedic Surgery Artificial Intelligence Laboratory, Department of Orthopedic Surgery (B.K., J.P.M., P.R., M.J.T., A.N.L., C.C.W.), Radiology Informatics Laboratory, Department of Radiology (B.K., P.R., B.J.E.), Department of Orthopedic Surgery (M.J.T., A.N.L., C.C.W.), and Department of Clinical Anatomy (C.C.W.), Mayo Clinic, 200 1st St SW, Rochester, MN 55905.

Radiology. Artificial intelligence
|December 11, 2023
PubMed
概括
此摘要是机器生成的。

一个深度学习算法有效地从医疗图像中删除放射性标记,从而实现非识别数据共享. 这种监督学习方法确保了患者的隐私,同时保留了诸如横向标记器之类的有用信息.

关键词:
传统的X光学X光学卷积神经网络 (CNN) 是一种神经网络.实验调查 实验调查骨架与轴的关系监督学习 监督学习胸部 胸部 胸部 胸部转移学习 转移学习

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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相关实验视频

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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科学领域:

  • 医疗成像医学成像
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 医疗图像中的放射标记包含受保护的健康信息.
  • 删除这些数据对于非身份化数据共享和研究至关重要.
  • 现有的方法可能不够有效或足够准确,无法进行全面的非识别.

研究的目的:

  • 开发和验证用于定位和删除放射标记的深度学习算法.
  • 为了使医疗放射图片的安全无标识数据共享.
  • 在内部和外部数据集上评估算法的准确性和有效性.

主要方法:

  • 2000张关节和骨盆X射线图的注释,用于训练一个物体检测模型.
  • 使用涉及标记物定位和表征的双通道方法.
  • 使用卷积神经网络 (CNN) 的监督学习和转移学习.
  • 在外部数据集上验证有微调的胸部X射线图.

主要成果:

  • 在内部测试组上,在精度回忆曲线下的面积达到0.96.
  • 对于内部数据上的横向标记,已证明100%的非识别精度和93%的保留精度.
  • 外部验证显示96%的非识别准确度,在微调后改进到99.6%.
  • 该算法有效地删除了识别信息,同时保留了临床相关的标记.

结论:

  • 开发的深度学习算法在去识别放射图像方面非常有效.
  • 双通道方法提供了一个强大的解决方案,可以从医疗数据中删除受保护的健康信息.
  • 这项技术为研究和临床进展提供了安全的数据共享.