  Even though convolutional neural networks (CNNs) are driving progress in
medical image segmentation, standard models still have some drawbacks. First,
the use of multi-scale approaches, i.e., encoder-decoder architectures, leads
to a redundant use of information, where similar low-level features are
extracted multiple times at multiple scales. Second, long-range feature
dependencies are not efficiently modeled, resulting in non-optimal
discriminative feature representations associated with each semantic class. In
this paper we attempt to overcome these limitations with the proposed
architecture, by capturing richer contextual dependencies based on the use of
guided self-attention mechanisms. This approach is able to integrate local
features with their corresponding global dependencies, as well as highlight
interdependent channel maps in an adaptive manner. Further, the additional loss
between different modules guides the attention mechanisms to neglect irrelevant
information and focus on more discriminant regions of the image by emphasizing
relevant feature associations. We evaluate the proposed model in the context of
semantic segmentation on three different datasets: abdominal organs,
cardiovascular structures and brain tumors. A series of ablation experiments
support the importance of these attention modules in the proposed architecture.
In addition, compared to other state-of-the-art segmentation networks our model
yields better segmentation performance, increasing the accuracy of the
predictions while reducing the standard deviation. This demonstrates the
efficiency of our approach to generate precise and reliable automatic
segmentations of medical images. Our code is made publicly available at
https://github.com/sinAshish/Multi-Scale-Attention
