  Incorporating human domain knowledge for breast tumor diagnosis is
challenging, since shape, boundary, curvature, intensity, or other common
medical priors vary significantly across patients and cannot be employed. This
work proposes a new approach for integrating visual saliency into a deep
learning model for breast tumor segmentation in ultrasound images. Visual
saliency refers to image maps containing regions that are more likely to
attract radiologists visual attention. The proposed approach introduces
attention blocks into a U-Net architecture, and learns feature representations
that prioritize spatial regions with high saliency levels. The validation
results demonstrate increased accuracy for tumor segmentation relative to
models without salient attention layers. The approach achieved a Dice
similarity coefficient of 90.5 percent on a dataset of 510 images. The salient
attention model has potential to enhance accuracy and robustness in processing
medical images of other organs, by providing a means to incorporate
task-specific knowledge into deep learning architectures.
