CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation

Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they are still challenged by complicated conditions where the segmentation target has large variations of position, shape and scale, and existing CNNs have a poor explainability that limits their application to clinical decisions. In this work, we make extensive use of multiple attentions in a CNN architecture and propose a comprehensive attention-based CNN (CA-Net) for more accurate and explainable medical image segmentation that is aware of the most important spatial positions,…

• WT
  Wellcome Trust

  Award: 203148/Z/16/Z
• RA
  Royal Academy of Engineering

  Award: RCSRF18194
• NN
  National Natural Science Foundation of China

  Awards: 81771921, 61901084
• EA
  Engineering and Physical Sciences Research Council

  Awards: NS/A000049/1, NS/A000027/1