GraphCBMs extend concept bottleneck models by building latent concept graphs to model correlations between concepts, yielding better image classification accuracy, more informative structure for interpretability, and stronger intervention results.
Language in a bottle: Language model guided concept bottlenecks for interpretable image classification
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Uncertainty-aware extensions to Variational Information Pursuit (EUAV-IP and IUAV-IP) improve reliability and conciseness of concept-based predictions on five medical imaging datasets.
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Graph Concept Bottleneck Models
GraphCBMs extend concept bottleneck models by building latent concept graphs to model correlations between concepts, yielding better image classification accuracy, more informative structure for interpretability, and stronger intervention results.
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Uncertainty-Aware Information Pursuit for Interpretable and Reliable Medical Image Analysis
Uncertainty-aware extensions to Variational Information Pursuit (EUAV-IP and IUAV-IP) improve reliability and conciseness of concept-based predictions on five medical imaging datasets.