Learning How to MIMIC: Using Model Explanations to Guide Deep Learning Training

Abstract

Healthcare is seen as one of the most influential applications of Deep Learning (DL). Increasingly, DL models have been shown to achieve high-levels of performance on medical diagnosis tasks, in some cases achieving levels of performance on-par with medical experts. Yet, very few are deployed into real-life scenarios. One of the main reasons for this is the lack of trust in those models by medical professionals driven by the black-box nature of the deployed models. Numerous explainability techniques have been developed to alleviate this issue by providing a view on how the model reached a given decision. Recent studies have shown that those explanations can expose the models’ reliance on areas of the feature space that has no justifiable medical interpretation, widening the gap with the medical experts. In this paper we evaluate the deviation of saliency maps produced by DL classification models from radiologist’s eye-gaze while they study the MIMIC-CXR-EGD images, and we propose a novel model architecture that utilises model explanations during training only (i.e. not during inference) to improve the overall plausibility of the model explanations. We substantially improve the similarity between the model’s explanations and radiologists’ eye-gaze data, reducing Kullback-Leibler Divergence by 90% and increasing Normalised Scanpath Saliency by 216%. We argue that this significant improvement is an important step towards building more robust and interpretable DL solutions in health-care.

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