MOTIVATION
Single-cell foundation models (scFMs) are transforming computational biology by enabling generalizable, task-agnostic representations for versatile single-cell analysis. Despite their progress in facilitating rapid deployment for downstream tasks, off-the-shelf scFMs still have some overlooked concerns: (I)...
Tian-Yu Liu, Fan Zhang, Jia-Yuan Chen et al.· Bioinformatics· 0 citations
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...
Zi-Jie Meng, Xi-Wei Dai, Ying-Ying Zhang et al.· 0 citations
Dynamic Evidence-Guided Preference Optimization (DEPO) is proposed, a new framework that enables evidence-aware and adaptive preference learning for Med-LVLMs and introduces Multi-Modal Evidence Perturbation (MEP) to suppress non-causal textual and visual shortcuts and Dispre-ferred Evidence Resampling (DER) to continu...
Zixuan Huang, Zhihong Zhu, Xiaolong Liu et al.· Annual Meeting of the Associ...· 0 citations
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.
Zi-Jie Meng, Yun-Chen Zhang, Hualiang Wang et al.· 0 citations
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