Summary of How Does Diverse Interpretability Of Textual Prompts Impact Medical Vision-language Zero-shot Tasks?, by Sicheng Wang et al.
How Does Diverse Interpretability of Textual Prompts Impact Medical Vision-Language Zero-Shot Tasks?
by Sicheng Wang, Che Liu, Rossella Arcucci
First submitted to arxiv on: 31 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); 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 Recent advancements in medical vision-language pre-training (MedVLP) have improved zero-shot medical image classification tasks by leveraging large-scale medical image-text pairs. However, the performance of these tasks can be heavily influenced by the variability in textual prompts describing the categories, requiring robustness in MedVLP models to diverse prompt styles. This study assesses the sensitivity of three widely-used MedVLP methods to a variety of prompts across 15 different diseases, using six unique prompt styles mirroring real clinical scenarios. The findings indicate that all MedVLP models show unstable performance across different prompt styles, suggesting a lack of robustness. Additionally, the models’ performance varies with increasing prompt interpretability, revealing difficulties in comprehending complex medical concepts. This study highlights the need for further development in MedVLP methodologies to enhance their robustness to diverse zero-shot prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A recent study looked at how well computers can understand medical images and texts without being taught beforehand. They found that even with advanced training, these computer models are not very good at understanding different types of text prompts about the same image. The study shows that these models do better when the text prompts are more straightforward, but struggle to understand complex or tricky prompts. This means that we need to improve how these computer models work so they can be more reliable and accurate in real-world medical situations. |
Keywords
» Artificial intelligence » Image classification » Prompt » Zero shot
