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Recurrent Longitudinal Memory (ReLMem), a framework that learns to maintain fixed-capacity patient memory for efficient downstream prediction with a frozen LLM, is introduced and a multi-granularity optimization strategy to preserve task-relevant information throughout recurrent updates and support downstream predictio...
This work presents MedUP, a Med-VLM that natively unifies perception and understanding within a shared token space, and curates UniMed-Train, a 1.84M-instance corpus spanning text-guided segmentation, region-grounded understanding, medical VQA and CoT-based segmentation, and introduces UniMed-Bench for unified evaluati...
This work develops MedUAG, an end-to-end trained unified medical model that achieves strong performance across a wide array of understanding and generation tasks, establishing a competitive baseline and paving the way for next-generation medical multimodal systems.
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