1. Example of a simple deep network architecture.
Example of a simple deep network architecture. The goal of this network is to classify MR images into 4 specific diagnoses (normal, tumor, stroke, hemorrhage). Multiple different images form the training set. For each new case, the image is broken down into its constituent voxels, each one of which acts as an input into the network. This example has 3 hidden layers with 7 neurons in each layer, and the final output is the probabilities of the 4 classification states. All layers are fully connected. At the bottom is a zoomed-in view of an individual neuron in the second hidden layer, which receives input from the previous layer, performs a standard matrix multiplication (including a bias term), passes this through a nonlinear function (the rectified linear unit function in this example), and outputs a single value to all the neurons of the next layer.
G. Zaharchuk et al. AJNR Am J Neuroradiol doi: /ajnr.A5543
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