A method that produces minimal edits to source samples via diffusion autoencoder and MMD optimization, raising p-values to show statistical closeness to a target group in deep two-sample testing.
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3 Pith papers cite this work, alongside 190 external citations. Polarity classification is still indexing.
3
Pith papers citing it
190
external citations · external index
years
2026 3representative citing papers
Existing visual attribution methods often fail to identify the visual evidence used by LVLMs in chest X-ray reasoning, while MedFocus using unbalanced optimal transport and targeted interventions substantially outperforms them across multiple models and settings.
The abstract announces a systematic review and 93% pooled balanced accuracy that the full text never presents; the body only gives a qualitative framework.
citing papers explorer
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Counterfactual Explanations for Deep Two-Sample Testing
A method that produces minimal edits to source samples via diffusion autoencoder and MMD optimization, raising p-values to show statistical closeness to a target group in deep two-sample testing.
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Rethinking Visual Attribution for Chest X-ray Reasoning in Large Vision Language Models
Existing visual attribution methods often fail to identify the visual evidence used by LVLMs in chest X-ray reasoning, while MedFocus using unbalanced optimal transport and targeted interventions substantially outperforms them across multiple models and settings.
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Uncertainty-Calibrated Explainable Artificial Intelligence for Fetal Ultrasound Plane Classification: A Systematic Review
The abstract announces a systematic review and 93% pooled balanced accuracy that the full text never presents; the body only gives a qualitative framework.