  Over the last years, many 'explainable artificial intelligence' (xAI)
approaches have been developed, but these have not always been objectively
evaluated. To evaluate the quality of heatmaps generated by various saliency
methods, we developed a framework to generate artificial data with synthetic
lesions and a known ground truth map. Using this framework, we evaluated two
data sets with different backgrounds, Perlin noise and 2D brain MRI slices, and
found that the heatmaps vary strongly between saliency methods and backgrounds.
We strongly encourage further evaluation of saliency maps and xAI methods using
this framework before applying these in clinical or other safety-critical
settings.
