Deep Learning in Medical Image Analysis

Convolutional Neural Networks

In the deep models of SAEs, DBNs, and DBMs, described above, the inputs are always in vector form. However, for (medical) images, the structural information among neighboring pixels or voxels is also important, but vectorization inevitably destroys such structural and configural in- formation in images. CNNs (76) are designed to better utilize spatial and configural information by taking 2D or 3D images as input. Structurally, CNNs have convolutional layers interspersed with pooling layers, followed by fully connected layers as in a standard multilayer neural network. Unlike a deep neural network, a CNN exploits three mechanisms—a local receptive field, weight sharing, and subsampling (Figure 3)—that greatly reduce the degrees of freedom in a model.
The role of a convolutional layer is to detect local features at different positions in the input

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j

ij
feature maps with learnable kernels k^(l), namely connection weights between the feature map i at layer l 1 and the feature map j at layer l . Specifically, the units of the convolutional layer l compute their activation A^(l) on the basis of only a spatially contiguous subset of units in the feature
maps A^(l−1) of the preceding layer l − 1 by convolving the kernels k^(l) as follows:

i
(l )
^⎛_M (l −1)
(l −1)


(l )
ij
(l )⎞