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Saliency maps have become a widely used method to assess which areas of theinput 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 knowledgethat has examined the efficacy of these techniques and quantified them usingoverlap with ground truth bounding boxes. In this work, we explored thecredibility of the various existing saliency map methods on the RSNA Pneumoniadataset. We found that GradCAM was the most sensitive to model parameter andlabel randomization, and was highly agnostic to model architecture.

Assessing the validity of saliency maps for abnormality localization in medical imaging