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The recently emerged field of explainable artificial intelligence (XAI) attempts to shed lights on 'black box' Machine Learning (ML) models in understandable terms for human. As several explanation methods are developed alongside different applications for a black box model, the need for expert-level evaluation in inspecting their effectiveness becomes inevitable. This is significantly important for sensitive domains such as medical applications where evaluation of experts is essential to better understand how accurate the results of complex ML are and debug the models if necessary. The aim of this study is to experimentally show how the expert-level evaluation of XAI methods in a medical application can be utilized and aligned with the actual explanations generated by the clinician. To this end, we collect annotations from expert subjects equipped with an eye-tracker while they classify medical images and devise an approach for comparing the results with those obtained from XAI methods. We demonstrate the effectiveness of our approach in several experiments.

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A new brand of technical artificial intelligence ( Explainable AI ) research has focused on trying to open up the 'black box' and provide some explainability. This paper presents a novel visual explanation method for deep leaning networks in the form of a saliency map that can effectively localize entire object regions. In contrast to the current state-of-the art methods, the proposed method shows quite promising visual explanations that can gain greater trust of human expert. Both quantitative and qualitative evaluations are carried out on both general and clinical data sets to confirm the effectiveness of the proposed method.