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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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在医学图像分析中的浅层和深度学习分类器.

Francesco Prinzi1,2, Tiziana Currieri1, Salvatore Gaglio3,4

  • 1Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy.

European radiology experimental
|March 4, 2024
PubMed
概括

人工智能 (AI) 和机器学习 (ML) 正在通过临床决策支持的预测模型彻底改变医学. 本综述探讨了放射学中的浅层和深度学习分类器,根据任务,数据和可解释性需求指导选择.

关键词:
人工智能的人工智能是人工智能.深度学习是一种深度学习.可解释的人工智能机器学习分类器机器学习分类器浅层学习是一种浅层的学习.

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科学领域:

  • 医疗人工智能 医疗人工智能
  • 医疗保健中的机器学习
  • 放射学 信息学 信息学

背景情况:

  • 人工智能 (AI) 和医学之间的协同作用正在迅速推进医生决策的预测建模.
  • 机器学习 (ML) 是人工智能的子分支,在开发这些临床决策支持系统方面至关重要.
  • 了解ML分类器对于其在医疗保健中的应用至关重要,特别是在放射学中.

研究的目的:

  • 为放射学中可访问和广泛使用的机器学习分类器提供教育见解.
  • 区分传统 (浅层) 和深度学习架构.
  • 根据特定的临床需求和资源,为选择合适的分类器提供准则.

主要方法:

  • 浅层学习算法的审查和比较 (支持矢量机器,随机森林,XGBoost).
  • 对深度学习架构 (卷积神经网络,视觉转换器) 的审查和比较.
  • 讨论分类器训练和特征提取的关键步骤 (例如,用于浅层学习的放射学).

主要成果:

  • 浅分类器需要从感兴趣的区域手动提取特征.
  • 深度分类器自动化特征提取和分类过程.
  • 分类器的选择取决于任务要求,数据集大小,可解释性需求和计算资源.

结论:

  • 人工智能和机器学习,特别是各种分类器,为提高放射学临床决策支持提供了巨大的潜力.
  • 选择分类器涉及平衡性能与数据可用性和可解释性等实际考虑.
  • 人工智能模型的可解释性对于它们可靠地融入临床实践至关重要.