Summary of Beyond Human Vision: the Role Of Large Vision Language Models in Microscope Image Analysis, by Prateek Verma et al.
Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis
by Prateek Verma, Minh-Hao Van, Xintao Wu
First submitted to arxiv on: 1 May 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 This paper explores the application of vision language models (VLMs) to medical image analysis. VLMs like LLaVA, ChatGPT-4, Gemini, and Segment Anything Model (SAM) have shown impressive performance on natural image captioning, visual question answering, and spatial reasoning tasks. The authors test these models’ performance on classification, segmentation, counting, and visual question answering tasks using microscopy images. While the models can comprehend visual features in microscopy images, they struggle with impurities, defects, artefact overlaps, and diversity present in the images. This study highlights the need for domain expertise to achieve accurate results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computer programs called vision language models (VLMs) can help doctors and scientists understand medical images better. These VLMs are good at understanding pictures and words, but they’re not perfect. The researchers tested these models on medical images and found that while they can do some things well, they make mistakes when the images are complicated or have imperfections. This study shows that we need experts to help computers understand medical images accurately. |
Keywords
» Artificial intelligence » Classification » Gemini » Image captioning » Question answering » Sam
