MedUAG: Unified Understanding and Generation for Medical Multimodal Models
Recent Multimodal Large Language Models (MLLMs) are rapidly evolving into unified understanding and generation (UAG) frameworks. However, extending these unified paradigms to the medical domain is hindered by: the absence of comprehensive training and evaluation benchmarks, and the lack of broadly validated unified medical model. To address these gaps, we present a comprehensive foundation for medical UAG. First, we construct MedUAGCorpus, the largest unified medical understanding and generation dataset to date, comprising over 6 million instances across 14 imaging modalities. Second, we intro
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- FuzzyOverlapping authors or contributors · 62%affaan-m/ECC →
“Shared author/contributor keys: jiang”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%BerriAI/litellm →
“Shared author/contributor keys: jiang”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
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- FuzzyOverlapping authors or contributors · 62%modular/modular →
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- LinkedLinked via arxiv author · 85%Zijie Meng →
“MedUAG: Unified Understanding and Generation for Medical Multimodal Models”
- LinkedLinked via arxiv author · 85%Yuncheng Zhang →
“MedUAG: Unified Understanding and Generation for Medical Multimodal Models”
- LinkedLinked via arxiv author · 85%Hualiang Wang →
“MedUAG: Unified Understanding and Generation for Medical Multimodal Models”