LePaX enables high-resolution chest X-ray report generation by learning to allocate resolution to diagnostically relevant regions and fusing high-res patches back into global features without increasing token count.
In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
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AtomiMed is a new modality-agnostic evaluation framework for medical report generation that decomposes reports into hierarchical atomic clinical facts and applies agentic cross-verification to achieve higher correlation with radiologist judgments than n-gram metrics.
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Seeing What Matters: Lesion-Aware High-Resolution Patch Discovery and Fusion for Chest X-ray Report Generation
LePaX enables high-resolution chest X-ray report generation by learning to allocate resolution to diagnostically relevant regions and fusing high-res patches back into global features without increasing token count.
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AtomiMed: Hierarchical Atomic Fact-Checking for Universal Clinical-Aware Medical Report Evaluation
AtomiMed is a new modality-agnostic evaluation framework for medical report generation that decomposes reports into hierarchical atomic clinical facts and applies agentic cross-verification to achieve higher correlation with radiologist judgments than n-gram metrics.