  We propose a novel technique to incorporate attention within convolutional
neural networks using feature maps generated by a separate convolutional
autoencoder. Our attention architecture is well suited for incorporation with
deep convolutional networks. We evaluate our model on benchmark segmentation
datasets in skin cancer segmentation and lung lesion segmentation. Results show
highly competitive performance when compared with U-Net and it's residual
variant.
