Fig. 5.

Schematic of the deep neural network. a The 12 Channel Image is loaded into a fully convolutional network with six convolutional and max-pooling layers (not shown for simplicity). The output is a 1D map of ER predictions, which is averaged and normalized (not shown) to produce an ER score for the image. The size of the matrix that holds the convolutional weights is indicated in red, where a matrix N × C × X × Y has N Kernels that act on a C channel input of size X × Y × C. b An example of convolutional and max pooling operations. In convolution, the starting image (left) is convolved by four kernels (middle) to produce four feature maps (right). In max pooling, the maximum value of each 2 × 2 square is used to produce an output image