  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
learning 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.
