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Towards greater neuroimaging classification transparency via the integration of explainability methods and confidence estimation approaches

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Informatics in Medicine Unlocked 2023年 37卷 101176页

作者： Ellis, Charles A. Miller, Robyn L. Calhoun, Vince D. Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University 313 Ferst Dr NW Atlanta 30332 GA United States Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology Emory University 55 Park Pl NE Atlanta 30303 GA United States Department of Computer Science Georgia State University 25 Park PlaceSuite 700 Atlanta 30303 GA United States

The field of neuroimaging has increasingly sought to develop artificial intelligence-based models for neurological and neuropsychiatric disorder automated diagnosis and clinical decision support. However, if these models are to be implemented in a clinical setting, transparency will be vital. Two aspects of transparency are (1) confidence estimation and (2) explainability. Confidence estimation approaches indicate confidence in individual predictions. explainability methods give insight into the importance of features to model predictions. In this study, we integrate confidence estimation and explainability approaches for the first time. We demonstrate their viability for schizophrenia diagnosis using resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data. We compare two confidence estimation approaches: Monte Carlo dropout (MCD) and MC batch normalization (MCBN). We combine them with two gradient-based explainability approaches, saliency and layer-wise relevance propagation (LRP), and examine their effects upon explanations. We find that MCD often adversely affects model gradients, making it ill-suited for integration with gradient-based explainability methods. In contrast, MCBN does not affect model gradients. Additionally, we find many participant-level differences between regular explanations and the distributions of explanations for combined explainability and confidence estimation approaches. This suggests that a similar confidence estimation approach used in a clinical context with explanations only output for the regular model would likely not yield adequate explanations. We hope that our findings will provide a starting point for the integration of the two fields, provide useful guidance for future studies, and accelerate the development of transparent neuroimaging clinical decision support systems. © 2023 The Authors

关键词： Clinical decision support systems Explainable artificial intelligence gradient-based explainability methods Monte Carlo batch normalization Monte Carlo dropout Neuroimaging

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