This Changes to That : Combining Causal and Non-Causal Explanations to Generate Disease Progression in Capsule Endoscopy

The need to understand the decision-making mechanisms of deep learning networks has led to a growing effort in exploring both modal-dependent and model-agnostic research methods. 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 the Wireless Capsule Endoscopy (WCE) domain to illustrate the effectiveness of our explanations. The saliency maps generated by our approach are competitive on the softmax information score.