  Spatial attention has been introduced to convolutional neural networks (CNNs)
for improving both their performance and interpretability in visual tasks
including image classification. The essence of the spatial attention is to
learn a weight map which represents the relative importance of activations
within the same layer or channel. All existing attention mechanisms are local
attentions in the sense that weight maps are image-specific. However, in the
medical field, there are cases that all the images should share the same weight
map because the set of images record the same kind of symptom related to the
same object and thereby share the same structural content. In this paper, we
thus propose a novel global spatial attention mechanism in CNNs mainly for
medical image classification. The global weight map is instantiated by a
decision boundary between important pixels and unimportant pixels. And we
propose to realize the decision boundary by a binary classifier in which the
intensities of all images at a pixel are the features of the pixel. The binary
classification is integrated into an image classification CNN and is to be
optimized together with the CNN. Experiments on two medical image datasets and
one facial expression dataset showed that with the proposed attention, not only
the performance of four powerful CNNs which are GoogleNet, VGG, ResNet, and
DenseNet can be improved, but also meaningful attended regions can be obtained,
which is beneficial for understanding the content of images of a domain.
