  Convolutional neural networks (CNN) are now being widely used for classifying
and detecting pulmonary abnormalities in chest radiographs. Two complementary
generalization properties of CNNs, translation invariance and equivariance, are
particularly useful in detecting manifested abnormalities associated with
pulmonary disease, regardless of their spatial locations within the image.
However, these properties also come with the loss of exact spatial information
and global relative positions of abnormalities detected in local regions.
Global relative positions of such abnormalities may help distinguish similar
conditions, such as COVID-19 and viral pneumonia. In such instances, a global
attention mechanism is needed, which CNNs do not support in their traditional
architectures that aim for generalization afforded by translation invariance
and equivariance. Vision Transformers provide a global attention mechanism, but
lack translation invariance and equivariance, requiring significantly more
training data samples to match generalization of CNNs. To address the loss of
spatial information and global relations between features, while preserving the
inductive biases of CNNs, we present a novel technique that serves as an
auxiliary attention mechanism to existing CNN architectures, in order to
extract global correlations between salient features.
