Attention gated networks: Learning to leverage salient regions in medical images

We propose a novel attention gate (AG) model for medical image analysis 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 when using convolutional neural networks (CNNs). AGs can be easily integrated into standard CNN models such as VGG or U-Net architectures with minimal computational overhead while increasing the model sensitivity and prediction accuracy. The proposed AG models are evaluated on a variety of tasks,…

• IO
  Institute of Ergonomics and Human Factors

  Award: 102431
• EA
  Engineering and Physical Sciences Research Council

  Awards: EP/P001009/1, EP/L016796/1, EP/P001009/1