@inproceedings{
laguna2023explimeable,
title={Exp{LIME}able: An exploratory framework for {LIME}},
author={Sonia Laguna and Julian Heidenreich and Jiugeng Sun and Nil{\"u}fer Cetin and Ibrahim Al Hazwani and Udo Schlegel and Furui Cheng and Mennatallah El-Assady},
booktitle={XAI in Action: Past, Present, and Future Applications},
year={2023},
url={https://openreview.net/forum?id=81FSrQxgEv}
}
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, \textit{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.
ExpLIMEable: An exploratory framework for LIME
Sonia Laguna Julian Heidenreich Jiugeng Sun Nilüfer Cetin Ibrahim Al Hazwani Udo Schlegel Furui Cheng Mennatallah El-Assady