Causal Scoring Medical Image Explanations: A Case Study On Ex-vivo
  Kidney Stone Images
Armando Villegas-Jimenez Daniel Flores-Araiza Francisco Lopez-Tiro Gilberto Ochoa-Ruiz andand Christian Daul
On the promise that if human users know the cause of an output, it would
enable them to grasp the process responsible for the output, and hence provide
understanding, many explainable methods have been proposed to indicate the
cause for the output of a model based on its input. Nonetheless, little has
been reported on quantitative measurements of such causal relationships between
the inputs, the explanations, and the outputs of a model, leaving the
assessment to the user, independent of his level of expertise in the subject.
To address this situation, we explore a technique for measuring the causal
relationship between the features from the area of the object of interest in
the images of a class and the output of a classifier. Our experiments indicate
improvement in the causal relationships measured when the area of the object of
interest per class is indicated by a mask from an explainable method than when
it is indicated by human annotators. Hence the chosen name of Causal
Explanation Score (CaES)