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AI for Medicine - Reading Notes
Yuanzhe (Roger) Li
2020 May
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Notes on some of the recommended readings from the specialization.

  • Materials
  • Technical notes
    • Architecture

      3D U-Net

      • Contraction: blocks of 3x3 conv. layers followed by 2x2 max pooling, with the number of feature maps doubles after each block to increase "what" (complex structure) and reduce "where".
      • Bottleneck: mediates between the contraction and expansion layers.
      • Expansion: blocks of 3x3 conv. layers followed by 2x2 up-sampling layers, with the number of feature maps halved after each block to maintain symmetry (for concatenation).
    • Transposed convolution (up-sampling)

      • A transposed convolution is a convolution where the implementation of the forward and backward passes are swapped to achieve effective up-sampling. It is commonly used in semantic segmentation tasks which requires to predict values for each pixel.
      • See slides from INFO8010 deep learning course, and the tutorial A guide to convolution arithmetic for deep learning for details.
    • Loss function

      • Pixel-wise soft-max over the final feature map combined with cross entropy.
    • The U-Net paper uses warping error for evaluation.

      • The warping error between two segmentations is the minimum mean square error between the pixels of the target segmentation and the pixels of a topology-preserving warped source segmentation.
      • Mathematically, warping error is defined as $D(T||L^) = \min_{L <| L^} ||T-L||^2$, where $L^$ is the ground truth labeling, $T$ is a candidate labeling, and $L$ is any warping of $L^$.
      • See article Segmentation Metrics for details about Pixel/Warping/Rand errors.