Summary of Simplifying Multimodality: Unimodal Approach to Multimodal Challenges in Radiology with General-domain Large Language Model, by Seonhee Cho et al.
Simplifying Multimodality: Unimodal Approach to Multimodal Challenges in Radiology with General-Domain Large Language Model
by Seonhee Cho, Choonghan Kim, Jiho Lee, Chetan Chilkunda, Sujin Choi, Joo Heung Yoon
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 This paper proposes a novel framework called MID-M that leverages general-domain Large Language Models (LLMs) for processing multimodal data via image descriptions. The framework achieves comparable or superior performance to task-specific fine-tuned LMMs and other general-domain ones, with significantly fewer parameters. This is particularly relevant to the medical domain, where high-quality data poses unique challenges for model training and application. MID-M’s robustness against data quality issues highlights its practical utility in real-world medical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MID-M is a new way to use language models to understand images. It uses a general-purpose language model that doesn’t need to be trained on medical data to do well. This is important because medical data can be hard to get and train models on, but MID-M can still work well even with imperfect or incomplete data. This makes it a useful tool for doctors and researchers who want to use language models in their work. |
Keywords
» Artificial intelligence » Language model
