  Due to the unequivocal need for understanding the decision processes of deep
learning networks, both modal-dependent and model-agnostic techniques have
become very popular. Although both of these ideas provide transparency for
automated decision making, most methodologies focus on either using the
modal-gradients (model-dependent) or ignoring the model internal states and
reasoning with a model's behavior/outcome (model-agnostic) to instances. In
this work, we propose a unified explanation approach that given an instance
combines both model-dependent and agnostic explanations to produce an
explanation set. The generated explanations are not only consistent in the
neighborhood of a sample but can highlight causal relationships between image
content and the outcome. We use Wireless Capsule Endoscopy (WCE) domain to
illustrate the effectiveness of our explanations. The saliency maps generated
by our approach are comparable or better on the softmax information score.
