Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoE
<|reference_start|>Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoE: Multi-modal large language models (MLLMs) have shown impressive capabilities as a general-purpose interface for various visual and linguistic tasks. However, building a unified MLLM for multi-task learning in the medical field remains a thorny challenge. To mitigate the tug-of-war problem of multi-modal multi-task optimization, recent advances primarily focus on improving the LLM components, while neglecting the connector that bridges the gap between modalities. In this paper, we introduce Uni-Med, a novel medical generalist foundation model which consists of a universal visual feature extraction module, a connector mixture-of-experts (CMoE) module, and an LLM. Benefiting from the proposed CMoE that leverages a well-designed router with a mixture of projection experts at the connector, Uni-Med achieves efficient solution to the tug-of-war problem and can perform six different medical tasks including question answering, visual question answering, report generation, referring expression comprehension, referring expression generation and image classification. To the best of our knowledge, Uni-Med is the first effort to tackle multi-task interference at the connector. Extensive ablation experiments validate the effectiveness of introducing CMoE under any configuration, with up to an average 8% performance gains. We further provide interpretation analysis of the tug-of-war problem from the perspective of gradient optimization and parameter statistics. Compared to previous state-of-the-art medical MLLMs, Uni-Med achieves competitive or superior evaluation metrics on diverse tasks. Code, data and model will be soon available at GitHub.<|reference_end|>
@article{zhu2024uni-med:,
    title={Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task
  Learning Via Connector-MoE},
    author={Xun Zhu, Ying Hu, Fanbin Mo, Miao Li, Ji Wu},
    journal={arXiv preprint arXiv:2409.17508},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17508},
    primaryClass={cs.CV cs.AI cs.LG}
}