  Saliency maps have become a widely used method to assess which areas of the
input image are most pertinent to the prediction of a trained neural network.
However, in the context of medical imaging, there is no study to our knowledge
that has examined the efficacy of these techniques and quantified them using
overlap with ground truth bounding boxes. In this work, we explored the
credibility of the various existing saliency map methods on the RSNA Pneumonia
dataset. We found that GradCAM was the most sensitive to model parameter and
label randomization, and was highly agnostic to model architecture.
