Counterfactual baselines for Integrated Gradients yield more faithful and medically relevant attributions than standard baselines across three medical datasets.
Survey of Explainable AI Techniques in Healthcare
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Reveal-to-Revise integrates cross-modal attention fusion, Grad-CAM++ attribution, and bias feedback in a conditional attention WGAN-GP to report high accuracy, F1, and fairness metrics on multimodal MNIST variants and toxic text tasks.
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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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Reveal-to-Revise: Explainable Bias-Aware Generative Modeling with Multimodal Attention
Reveal-to-Revise integrates cross-modal attention fusion, Grad-CAM++ attribution, and bias feedback in a conditional attention WGAN-GP to report high accuracy, F1, and fairness metrics on multimodal MNIST variants and toxic text tasks.