  The increased availability and accuracy of eye-gaze tracking technology has
sparked attention-related research in psychology, neuroscience, and, more
recently, computer vision and artificial intelligence. The attention mechanism
in artificial neural networks is known to improve learning tasks. However, no
previous research has combined the network attention and human attention. This
paper describes a gaze-guided class activation mapping (GG-CAM) method to
directly regulate the formation of network attention based on expert
radiologists' visual attention for the chest X-ray pathology classification
problem, which remains challenging due to the complex and often nuanced
differences among images. GG-CAM is a lightweight ($3$ additional trainable
parameters for regulating the learning process) and generic extension that can
be easily applied to most classification convolutional neural networks (CNN).
GG-CAM-modified CNNs do not require human attention as an input when fully
trained. Comparative experiments suggest that two standard CNNs with the GG-CAM
extension achieve significantly greater classification performance. The median
area under the curve (AUC) metrics for ResNet50 increases from $0.721$ to
$0.776$. For EfficientNetv2 (s), the median AUC increases from $0.723$ to
$0.801$. The GG-CAM also brings better interpretability of the network that
facilitates the weakly-supervised pathology localization and analysis.
