Fig. 6.

The deep CNNs for predicting voxel-wise tissue outcome: (a) 2-D deep CNN, (b) 3-D deep CNN, and (c) the proposed deep CNN (denoted as “unit CNN-contralateral”); the first layer is the new convolutional layer that learns paired unit temporal filters for comparing the patch of interest and its contralateral patch, which is followed by a nonlinear layer (ReLU) and then the standard 2-D deep CNN. These deep CNNs learn feature filters to generate 128 complex hierarchical features in the last fully connected layer, which are then used by the softmax classifier to predict outcome. Abbreviations: conv, convolutional layer; max-pool, max-pooling layer; full, fully connected layer; softmax, softmax classifier, batch norm (batch normalization).