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.
Journal of the American Medical Informatics Association23(2), 304–310 (2016)
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Prototype-conditioned Mixture-of-Experts synthesizes missing modalities in federated learning and beats prior methods on heterogeneous chest X-ray clients without public data.
A dual-side evidence-injection method using ROI-guided modulation and semantic token mapping improves medical MLLM close-ended accuracy by up to 6% and cuts open-ended hallucinations by 35% across 5 datasets.
citing papers explorer
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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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ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities
Prototype-conditioned Mixture-of-Experts synthesizes missing modalities in federated learning and beats prior methods on heterogeneous chest X-ray clients without public data.
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Synergistic Perception-Reasoning Governance: Grounding Medical MLLMs with Verifiable Anatomical Evidence
A dual-side evidence-injection method using ROI-guided modulation and semantic token mapping improves medical MLLM close-ended accuracy by up to 6% and cuts open-ended hallucinations by 35% across 5 datasets.