ExpLIMEable: An exploratory framework for LIME

Abstract

ExpLIMEable is a tool to enhance the comprehension of Local Interpretable Model-Agnostic Explanations (LIME), particularly within the realm of medical image analysis. LIME explanations often lack robustness due to variances in perturbation techniques and interpretable function choices. Powered by a convolutional neural network for brain MRI tumor classification, ExpLIMEable seeks to mitigate these issues. This explainability tool allows users to tailor and explore the explanation space generated post hoc by different LIME parameters to gain deeper insights into the model’s decision-making process, its sensitivity, and limitations. We introduce a novel dimension reduction step on the perturbations seeking to find more informative neighborhood spaces and extensive provenance tracking to support the user. This contribution ultimately aims to enhance the robustness of explanations, key in high-risk domains like healthcare.