MRI Condition Monitoring with Explainable AI and Feature Selection

摘要: Monitoring multisensor MRI devices using anomaly detection for multivariate time series consists a challenging task. In this use case, we investigate and provide explanations for abnormalities detected in sensors during failed scans and propose a framework for MRI sensor condition monitoring using XAI and feature selection. Sensor properties are preprocessed (normalized, aggregated and resampled) and used to generate statistical features that are fed to machine learning models for Anomaly Detection (AD), namely Isolation Forest. A feature selection step using an XGBoost classifier is applied before the AD models to improve performance by removing unrelated sensors properties. Explanations for the total output of the models and selected instances are provided using SHAP (SHapley Additive exPlanations). Results are presented using plots with anomaly scores against scan statuses and SHAP explanations …