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We propose a novel attention gate (AG) model for medical image analysis thatautomatically learns to focus on target structures of varying shapes and sizes.Models trained with AGs implicitly learn to suppress irrelevant regions in aninput image while highlighting salient features useful for a specific task.This enables us to eliminate the necessity of using explicit externaltissue/organ localisation modules when using convolutional neural networks(CNNs). AGs can be easily integrated into standard CNN models such as VGG orU-Net architectures with minimal computational overhead while increasing themodel sensitivity and prediction accuracy. The proposed AG models are evaluatedon a variety of tasks, including medical image classification and segmentation.For classification, we demonstrate the use case of AGs in scan plane detectionfor fetal ultrasound screening. We show that the proposed attention mechanismcan provide efficient object localisation while improving the overallprediction performance by reducing false positives. For segmentation, theproposed architecture is evaluated on two large 3D CT abdominal datasets withmanual annotations for multiple organs. Experimental results show that AGmodels consistently improve the prediction performance of the basearchitectures across different datasets and training sizes while preservingcomputational efficiency. Moreover, AGs guide the model activations to befocused around salient regions, which provides better insights into how modelpredictions are made. The source code for the proposed AG models is publiclyavailable.

Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images