XIME3D:A Systematic Framework for Evaluating Explainable AI in 3D Medical Imaging under CT Image Pre-Processing Variations

Abstract

Recent advancements in deep learning have enabled expert-level performance in medical imaging for disease classification, but their black-box decision making processes limit trust in them and their wide-spread clinical deployment. While Explainable Artificial Intelligence (XAI) methods aim to bridge this gap, studies focus on 2D data or pre-processed research datasets that overlook the role of medical imaging pre-processing operations which is an essential component of real-world 3D medical imaging workflows. To address this limitation, we propose XIME3D, a systematic and predictive model–centered framework for evaluating explainability under realistic medical pre-processing conditions for volumetric medical data. The framework integrates five volumetric pre-processing variants and ten post-hoc attribution methods, evaluated through three complementary criteria: Correctness, Contrastivity, and Completeness, which together evaluate explanation dependence on model input, internal structure, and output behavior. Across more than 300 experimental configurations, XIME3D reveals that gradient-based methods, such as Integrated Gradients and Blur Integrated Gradients, provide the most consistent and model-aligned explanations, while noise-based approaches like SmoothGrad and VarGrad are less sensitive to model behavior. These findings emphasize the importance of clinically realistic evaluation pipelines for reliable explainability in 3D medical imaging.

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