RIHA proposes a hierarchical alignment transformer that uses multi-scale visual and textual feature pyramids plus optimal transport to generate more accurate radiology reports from medical images.
arXiv preprint arXiv:2410.03659 (2024) A Judge Reliability and Probe Detection Hall
2 Pith papers cite this work. Polarity classification is still indexing.
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Causal path-patching analysis across five MLLMs identifies distributed hallucination-driving attention heads and localized resisting heads whose imbalance biases generation toward erroneous text over visual evidence; a conditional intervention MACI suppresses the driving heads and cuts hallucination
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RIHA: Report-Image Hierarchical Alignment for Radiology Report Generation
RIHA proposes a hierarchical alignment transformer that uses multi-scale visual and textual feature pyramids plus optimal transport to generate more accurate radiology reports from medical images.
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Causal Evidence for Attention Head Imbalance in Modality Conflict Hallucination
Causal path-patching analysis across five MLLMs identifies distributed hallucination-driving attention heads and localized resisting heads whose imbalance biases generation toward erroneous text over visual evidence; a conditional intervention MACI suppresses the driving heads and cuts hallucination