Summary of Uni-med: a Unified Medical Generalist Foundation Model For Multi-task Learning Via Connector-moe, by Xun Zhu et al.
Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoE
by Xun Zhu, Ying Hu, Fanbin Mo, Miao Li, Ji Wu
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Uni-Med, a novel medical generalist foundation model that tackles the multi-modal multi-task optimization problem in large language models (LLMs). The proposed model consists of a universal visual feature extraction module, a connector mixture-of-experts (CMoE) module, and an LLM. By leveraging a well-designed router with a mixture of projection experts at the connector, Uni-Med efficiently solves the tug-of-war problem and performs six different medical tasks, including question answering, visual question answering, report generation, referring expression comprehension, referring expression generation, and image classification. The paper’s main contribution is the introduction of CMoE at the connector level to mitigate multi-task interference in MLLMs. Experimental results show that Uni-Med achieves up to an average 8% performance gains compared to previous state-of-the-art medical MLLMs on diverse tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new AI model called Uni-Med, which is designed to help doctors and researchers work more efficiently. Right now, there are many different ways that AI models can be used in medicine, but they often need to learn how to do each task separately. Uni-Med is special because it can learn to do multiple tasks at the same time, making it a very useful tool for medical professionals. |
Keywords
» Artificial intelligence » Feature extraction » Image classification » Mixture of experts » Multi modal » Multi task » Optimization » Question answering
