Evaluating saliency methods on artificial data with different background types

Céline Budding; Fabian Eitel; Kerstin Ritter; Stefan Haufe

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arXiv · 2112.04882

Evaluating saliency methods on artificial data with different background types

Céline Budding·Fabian Eitel·Kerstin Ritter·Stefan Haufe

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Abstract

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.

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eess.IV cs.CV cs.LG stat.ML

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BibTeX RIS

Céline Budding, Fabian Eitel, Kerstin Ritter, Stefan Haufe. 2021-12-09. Evaluating saliency methods on artificial data with different background types. https://arxiv.org/abs/2112.04882

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