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相关概念视频

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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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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Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
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Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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相关实验视频

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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对脑瘤MRI中异质结构细分的增量学习

Xiaofeng Liu1, Helen A Shih2, Fangxu Xing1

  • 1Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|April 26, 2024
PubMed
概括

这项研究引入了一种新的深度学习方法,用于不断更新医疗图像细分模型. 该方法有效地适应新的数据和结构,而不忘记先前的知识,从而使终身学习成为可能.

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科学领域:

  • 医疗图像分析 医学图像分析
  • 医疗保健中的人工智能
  • 计算机视觉 计算机视觉 计算机视觉

背景情况:

  • 静态深度学习 (DL) 模型在单域细分方面表现出色,但在不断变化的环境中失败.
  • 增量学习面临着诸如分布转移和没有源数据的新结构等挑战.
  • 灾难性遗忘阻碍了动态医疗数据场景中的模型更新.

研究的目的:

  • 开发一个统一的框架,逐步发展现成的细分模型,以不同的数据集与新的解剖学类别.
  • 在积累大医疗数据的背景下,为细分模型提供终身学习.
  • 在持续学习环境中应对分布转移和增量结构的挑战.

主要方法:

  • 提出了一个分歧意识的双流模块,具有刚性和可塑性分支,以连续批次重新规范化为指导.
  • 开发了一个辅助的伪标签训练方案,具有自我调节的动量混合衰变,用于自适应优化.
  • 评估了大脑瘤细分的框架,使用不断变化的MRI扫描仪/模式和增量结构.

主要成果:

  • 该框架成功地将细分模型演变为具有额外解剖类别的多种数据集.
  • 对于先前学习的结构来说,已证明保留了可区分性,减轻了灾难性遗忘.
  • 在动态脑瘤细分任务中实现了有效的终身模型扩展.

结论:

  • 拟议的框架提供了一种统一的方法,用于将静态DL细分模型适应不断变化的医疗数据.
  • 它可以实现对细分模型的现实终身延伸,这对于处理大规模,不断积累的医疗数据至关重要.
  • 该方法有效地平衡模型的可塑性和刚性,以便在医学成像中进行强大的增量学习.