Summary of On Large Visual Language Models For Medical Imaging Analysis: An Empirical Study, by Minh-hao Van et al.
On Large Visual Language Models for Medical Imaging Analysis: An Empirical Study
by Minh-Hao Van, Prateek Verma, Xintao Wu
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Medium Difficulty summary: Recently, large language models (LLMs) have gained popularity in natural language processing, while integrating them with vision enables users to explore emergent abilities with multimodal data. Visual language models (VLMs), such as LLaVA, Flamingo, or CLIP, have achieved impressive performance on various visio-linguistic tasks. The potential applications of large models in the biomedical imaging field are enormous. However, there is a lack of related work demonstrating the ability of large models to diagnose diseases. This study investigates the zero-shot and few-shot robustness of VLMs on medical imaging analysis tasks. Comprehensive experiments demonstrate the effectiveness of VLMs in analyzing biomedical images such as brain MRIs, microscopic images of blood cells, and chest X-rays. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine a super powerful computer that can understand and analyze pictures and words together. This is called a visual language model (VLM). It’s like having a smart assistant that can look at medical images, such as MRI scans or X-rays, and help doctors diagnose diseases. Right now, there isn’t much research on using these powerful models for medical imaging tasks. In this study, scientists tested how well VLMs perform when they don’t have any training data (zero-shot) or only a little training data (few-shot). The results show that VLMs are very good at analyzing biomedical images and can even help diagnose diseases. |
Keywords
» Artificial intelligence » Few shot » Language model » Natural language processing » Zero shot
