  eXplanation Based Learning (XBL) is a form of Interactive Machine Learning
(IML) that provides a model refining approach via user feedback collected on
model explanations. Although the interactivity of XBL promotes model
transparency, XBL requires a huge amount of user interaction and can become
expensive as feedback is in the form of detailed annotation rather than simple
category labelling which is more common in IML. This expense is exacerbated in
high stakes domains such as medical image classification. To reduce the effort
and expense of XBL we introduce a new approach that uses two input instances
and their corresponding Gradient Weighted Class Activation Mapping (GradCAM)
model explanations as exemplary explanations to implement XBL. Using a medical
image classification task, we demonstrate that, using minimal human input, our
approach produces improved explanations (+0.02, +3%) and achieves reduced
classification performance (-0.04, -4%) when compared against a model trained
without interactions.
