nnunet网络块

最新推荐文章于 2024-10-25 21:24:02 发布

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最新推荐文章于 2024-10-25 21:24:02 发布

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分类专栏：笔记文章标签： python

本文链接：https://blog.youkuaiyun.com/qq_34525135/article/details/123764955

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本文深入探讨了nnUNet的网络结构，包括其独特的设计特点和在图像分割任务中的优秀性能。nnUNet是一个基于Python的深度学习框架，专注于医学影像分析。文章详细解析了网络块的构成和工作原理，为读者提供了nnUNet实现高精度分割的洞察。

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Generic_UNet(
  (conv_blocks_localization): ModuleList(
    (0): Sequential(
      (0): StackedConvLayers(
        (blocks): Sequential(
          (0): ConvDropoutNormNonlin(
            (conv): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
            (instnorm): BatchNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
          )
        )
      )
      (1): StackedConvLayers(
        (blocks): Sequential(
          (0): ConvDropoutNormNonlin(
            (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
            (instnorm): BatchNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
          )
        )
      )
    )
    (1): Sequential(
      (0): StackedConvLayers(
        (blocks): Sequential(
          (0): ConvDropoutNormNonlin(
            (conv): Conv3d(640, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
            (instnorm): BatchNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
          )
        )
      )
      (1): StackedConvLayers(
        (blocks): Sequential(
          (0): ConvDropoutNormNonlin(
            (conv): Conv3d(320, 320, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
            (instnorm): BatchNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
          )
        )
      )
    )
    (2): Sequential(
      (0): StackedConvLayers(
        (blocks): Sequential(
          (0): ConvDropoutNormNonlin(
            (conv): Conv3d(512, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
            (instnorm): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
          )
        )
      )
      (1): StackedConvLayers(
        (blocks): Sequential(
          (0): ConvDropoutNormNonlin(
            (conv)