TY  - CONF
AU  - Ahmed M. A. Salih
AU  - Ilaria Boscolo Galazzo
AU  - Federica Cruciani
AU  - Lorenza Brusini
AU  - Petia Radeva
A2  - ICIP
PY  - 2022//
TI  - Investigating Explainable Artificial Intelligence for MRI-based Classification of Dementia: a New Stability Criterion for Explainable Methods
BT  - 29th IEEE International Conference on Image Processing
KW  - Image processing
KW  - Stability criteria
KW  - Machine learning
KW  - Robustness
KW  - Alzheimer's disease
KW  - Monitoring
N2  - Individuals diagnosed with Mild Cognitive Impairment (MCI) have shown an increased risk of developing Alzheimer’s Disease (AD). As such, early identification of dementia represents a key prognostic element, though hampered by complex disease patterns. Increasing efforts have focused on Machine Learning (ML) to build accurate classification models relying on a multitude of clinical/imaging variables. However, ML itself does not provide sensible explanations related to the model mechanism and feature contribution. Explainable Artificial Intelligence (XAI) represents the enabling technology in this framework, allowing to understand ML outcomes and derive human-understandable explanations. In this study, we aimed at exploring ML combined with MRI-based features and XAI to solve this classification problem and interpret the outcome. In particular, we propose a new method to assess the robustness of feature rankings provided by XAI methods, especially when multicollinearity exists. Our findings indicate that our method was able to disentangle the list of the informative features underlying dementia, with important implications for aiding personalized monitoring plans.
UR  - https://ieeexplore.ieee.org/abstract/document/9897253
UR  - http://dx.doi.org/10.1109/ICIP46576.2022.9897253
N1  - MILAB
ID  - Ahmed M. A. Salih2022
ER  -