  This article presents the prediction difference analysis method for
visualizing the response of a deep neural network to a specific input. When
classifying images, the method highlights areas in a given input image that
provide evidence for or against a certain class. It overcomes several
shortcoming of previous methods and provides great additional insight into the
decision making process of classifiers. Making neural network decisions
interpretable through visualization is important both to improve models and to
accelerate the adoption of black-box classifiers in application areas such as
medicine. We illustrate the method in experiments on natural images (ImageNet
data), as well as medical images (MRI brain scans).
