Figure 4:

Diagram of convolutional long short term memory network (CLSTM) architecture. The architecture uses 7 serial convolutional LSTM layers via 3×3 filters followed by the ReLU nonlinear activation function. Five sets of pre-contrast and post-contrast DCE images are used as inputs. The configuration of the dropout and down sampling are the same as in Figure 3. The number of the input channels is one. Five sets of pre-contrast and post-contrast DCE images are used as inputs, by adding them one by one into the CLSTM network. The number of activation channels in deeper layers is progressively increased from 4 to 8 to 16 to 32. The last dense layer is obtained by flattening the convolutional output feature maps from all states. Softmax is used as the activation function of the last fully connected layer.