Unveiling the decision making process in Alzheimer's disease diagnosis: A case-based counterfactual methodology for explainable deep learning.
Journal:
Journal of neuroscience methods
Published Date:
Nov 9, 2024
Abstract
BACKGROUND: The field of Alzheimer's disease (AD) diagnosis is undergoing significant transformation due to the application of deep learning (DL) models. While DL surpasses traditional machine learning in disease prediction from structural magnetic resonance imaging (sMRI), the lack of explainability limits clinical adoption. Counterfactual inference offers a way to integrate causal explanations into these models, enhancing their robustness and transparency.