Abstract
Deep learning process trace classification is proving powerful in several application domains, including medical ones; however, classification results are typically not explainable, an issue which is particularly relevant in medicine.
In our recent work we tackled this problem, by proposing trace saliency maps, a novel tool able to highlight what trace activities are particularly significant for the classification task. A trace saliency map is built by generating artificial perturbations of the trace at hand that are classified in the same class as the original one, called examples.
In this paper, we investigate the role of counterexamples (i.e., artificial perturbations that are classified in a different class with respect to the original trace) in refining trace saliency map information, thus improving explainability. We test the approach in the domain of stroke.
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Leonardi, G., Montani, S., Striani, M. (2023). Improving Stroke Trace Classification Explainability Through Counterexamples. In: Juarez, J.M., Marcos, M., Stiglic, G., Tucker, A. (eds) Artificial Intelligence in Medicine. AIME 2023. Lecture Notes in Computer Science(), vol 13897. Springer, Cham. https://doi.org/10.1007/978-3-031-34344-5_16
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DOI: https://doi.org/10.1007/978-3-031-34344-5_16
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