The C-Score quantifies intra-class explanation consistency for CAM methods via confidence-weighted pairwise soft IoU and detects AUC-consistency dissociation as an early warning for model instability on chest X-ray classification.
Explainable artifi- cial intelligence (XAI) in deep learning-based med- ical image analysis.Medical Image Analysis, 79: 102470
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A multi-agent AI framework using processing and acoustic agents achieves 91.6% accuracy and 0.821 F1 score for in-situ porosity defect detection in wire-arc additive manufacturing.
Counterfactual baselines for Integrated Gradients yield more faithful and medically relevant attributions than standard baselines across three medical datasets.
T-SHAP stabilizes SHAP attributions temporally for LSTM fall detection, achieving 94.3% accuracy and improved faithfulness on NTU RGB+D dataset.
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Quantifying Explanation Consistency: The C-Score Metric for CAM-Based Explainability in Medical Image Classification
The C-Score quantifies intra-class explanation consistency for CAM methods via confidence-weighted pairwise soft IoU and detects AUC-consistency dissociation as an early warning for model instability on chest X-ray classification.
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In-situ process monitoring for defect detection in wire-arc additive manufacturing: an agentic AI approach
A multi-agent AI framework using processing and acoustic agents achieves 91.6% accuracy and 0.821 F1 score for in-situ porosity defect detection in wire-arc additive manufacturing.
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On the notion of missingness for path attribution explainability methods in medical settings: Guiding the selection of medically meaningful baselines
Counterfactual baselines for Integrated Gradients yield more faithful and medically relevant attributions than standard baselines across three medical datasets.
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Explainable Fall Detection for Elderly Monitoring via Temporally Stable SHAP in Skeleton-Based Human Activity Recognition
T-SHAP stabilizes SHAP attributions temporally for LSTM fall detection, achieving 94.3% accuracy and improved faithfulness on NTU RGB+D dataset.