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对于比GPU内存大图像的精确基于片的细分推理.

Michael Possolo1, Peter Bajcsy1

  • 1National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.

Journal of research of the National Institute of Standards and Technology
|July 17, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了一种使用完全卷积神经网络 (FCN) 对大图像进行精确语义细分推断的制方法. 这种方法克服了GPU内存限制而不会影响结果,从而实现了整个幻灯片图像的分析.

关键词:
人工智能的人工智能是人工智能.卷积神经网络是一种卷积神经网络.有效的感受场有效的感受场.核心以外的加工加工.语义细分 语义细分 语义细分 语义细分

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科学领域:

  • 计算机视觉 计算机视觉
  • 医学图像分析 医学图像分析
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 完全卷积神经网络 (FCN) 能够对任意大图像进行语义细分,但受到GPU内存的限制.
  • 处理像全幻灯片显微镜图像这样的大图像需要外核方法来克服内存约束.

研究的目的:

  • 为了使精确的 (无错误) 外核语义细分推理任意大图像使用FCNs.
  • 为了克服GPU内存的限制,而不会在最终的细分结果中引入数值错误.

主要方法:

  • 一种使用光环边框围绕每块的瓦策略,瓦大小由GPU内存和网络接收场所决定.
  • 叠加输入光环并精确地在接处连接输出以确保连续性.
  • 在U-Net和FC-DenseNet架构上进行验证,包括量化与瓦相关的错误.

主要成果:

  • 记录了计算最佳尺寸和步伐的公式.
  • 提出的方法成功地对大型图像进行了精确的语义细分,在U-Net和FC-DenseNet.Net上进行了演示.
  • 违反约束的配置被量化为它们的错误影响.

结论:

  • 开发的切片方法有效地解决了基于FCN的大图像语义细分的GPU内存限制.
  • 这种方法适用于处理高分辨率的医疗图像,例如来自整片扫描仪的图像.
  • 该研究提供了一个框架,用于估计使用神经网络架构的有效受体场的化参数.