Summary of Tv-sam: Increasing Zero-shot Segmentation Performance on Multimodal Medical Images Using Gpt-4 Generated Descriptive Prompts Without Human Annotation, by Zekun Jiang et al.
TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human Annotation
by Zekun Jiang, Dongjie Cheng, Ziyuan Qin, Jun Gao, Qicheng Lao, Abdullaev Bakhrom Ismoilovich, Urazboev Gayrat, Yuldashov Elyorbek, Bekchanov Habibullo, Defu Tang, LinJing Wei, Kang Li, Le Zhang
First submitted to arxiv on: 24 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 The TV-SAM model is a novel multimodal medical image segmentation algorithm that uses a combination of large language models like GPT-4 and vision language models like GLIP to generate descriptive prompts for zero-shot segmentation. The model demonstrates strong performance on seven public datasets, outperforming existing methods like SAM AUTO and GSAM. Specifically, TV-SAM shows excellent results on the ISIC and WBC datasets, making it a promising tool for medical image analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research created an AI model called TV-SAM that can identify things in medical images without needing any special training. It works by combining different AI models to create text and visual prompts that help it understand what’s in the pictures. The researchers tested TV-SAM on many different medical image datasets and found that it did a great job, even better than some other methods. |
Keywords
» Artificial intelligence » Gpt » Image segmentation » Sam » Zero shot
