Summary of Improving Medical Diagnostics with Vision-language Models: Convex Hull-based Uncertainty Analysis, by Ferhat Ozgur Catak and Murat Kuzlu and Taylor Patrick
Improving Medical Diagnostics with Vision-Language Models: Convex Hull-Based Uncertainty Analysis
by Ferhat Ozgur Catak, Murat Kuzlu, Taylor Patrick
First submitted to arxiv on: 24 Nov 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 This paper proposes a novel approach to evaluate uncertainty in vision-language models (VLMs) applied to critical fields such as healthcare, which demands a high level of trust and reliability. The authors utilize the LLM-CXR model, a medical VLM, to generate responses for given prompts at different temperature settings. Results show that the LLM-CXR VLM exhibits high uncertainty at higher temperature settings, highlighting the importance of uncertainty in VLMs’ responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VLMs are powerful tools that can help us in many areas, like healthcare and education. However, we need to be sure they’re giving us good answers. This paper is about making sure these models are giving us accurate results by looking at how certain they are about their answers. The researchers used a special model called LLM-CXR to test this idea. They found that when the model is more uncertain, it’s actually giving better answers. |
Keywords
» Artificial intelligence » Temperature
