Summary of On the Compositional Generalization Of Multimodal Llms For Medical Imaging, by Zhenyang Cai et al.
On the Compositional Generalization of Multimodal LLMs for Medical Imaging
by Zhenyang Cai, Junying Chen, Rongsheng Wang, Weihong Wang, Yonglin Deng, Dingjie Song, Yize Chen, Zixu Zhang, Benyou Wang
First submitted to arxiv on: 28 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 investigates the capabilities of multimodal large language models (MLLMs) in medical domains, where limited data is often a challenge. While multi-task training has been shown to be effective, it overlooks internal relationships within tasks, leaving little guidance on selecting datasets for specific tasks. To address this, the authors employ compositional generalization (CG), which enables models to understand novel combinations by recombining learned elements. The paper assembles 106 medical datasets to create Med-MAT and demonstrates that MLLMs can use CG to generalize unseen medical images. The results show that CG is a key driver of generalization in multi-task training and supports datasets with limited data, highlighting its versatility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical experts are working on special computers called multimodal large language models (MLLMs) to help them understand medical pictures. But these computers need lots of information to learn, which can be hard to find in some areas of medicine. To solve this problem, scientists are trying to figure out how MLLMs work when they’re shown different types of medical images. They found that when MLLMs are trained on multiple tasks at once, they can understand new pictures better than when they’re only trained on one task. This is because the computers learn to recognize patterns in the images that help them make connections between different pieces of information. |
Keywords
» Artificial intelligence » Generalization » Multi task
