  We propose a novel attention gate (AG) model for medical imaging that
automatically learns to focus on target structures of varying shapes and sizes.
Models trained with AGs implicitly learn to suppress irrelevant regions in an
input image while highlighting salient features useful for a specific task.
This enables us to eliminate the necessity of using explicit external
tissue/organ localisation modules of cascaded convolutional neural networks
(CNNs). AGs can be easily integrated into standard CNN architectures such as
the U-Net model with minimal computational overhead while increasing the model
sensitivity and prediction accuracy. The proposed Attention U-Net architecture
is evaluated on two large CT abdominal datasets for multi-class image
segmentation. Experimental results show that AGs consistently improve the
prediction performance of U-Net across different datasets and training sizes
while preserving computational efficiency. The code for the proposed
architecture is publicly available.
