Toward an Explainable Artificial Intelligence Approach to Enhance Medical Imaging Classification Models

Abstract

Recent studies in eXplainable Artificial Intelligence (XAI) have been focusing on demystifying neural networks, which are considered algorithmic black-boxes. This is particularly important given the plethora of AI solutions, and in certain domains, such as healthcare and finance, due to regulation and compliance requirements. These XAI approaches yielded discussion points (e.g., evidence of model sufficiency), but overlooked connecting the explanations with steps to enhance model performances, as well as acquiring domain knowledge from them. The key difference is that the data enhancement strategies can improve model performance without re-training. In this study, we propose a novel XAI approach that utilizes visual explanations, in combination with domain knowledge, to guide data enhancement practices. The approach provides guidelines for future data enhancement, as well as knowledge elements in the form of explanation on the enhancement. We evaluated the proposed approach using a medical imaging dataset, and demonstrated the feasibility and effectiveness of the proposed approach.

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