We use a dataset of eye-tracking data from five radiologists to compare the regions used by deep learning models for their decisions and the heatmaps representing where radiologists looked. We conduct a class-independent analysis of the saliency maps generated by two methods selected from the literature: Grad-CAM and attention maps from an attention-gated model. For the comparison, we use shuffled metrics, avoiding biases from fixation locations. We achieve scores comparable to an interobserver…