Summary of Prompting Medical Large Vision-language Models to Diagnose Pathologies by Visual Question Answering, By Danfeng Guo and Demetri Terzopoulos
Prompting Medical Large Vision-Language Models to Diagnose Pathologies by Visual Question Answering
by Danfeng Guo, Demetri Terzopoulos
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 The abstract proposes two prompting strategies for Large Vision-Language Models (LVLMs) that extend to the medical domain, aiming to improve diagnostic performance on Visual Question Answering (VQA) tasks while reducing hallucination. The strategies provide detailed explanations of queried pathologies or fine-tune weak learners and textually provide their judgments to LVLMs. Tested on MIMIC-CXR-JPG and Chexpert datasets, the methods significantly improve the diagnostic F1 score, with a highest increase of 0.27. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical Vision-Language Models (LVLMs) are good at diagnosing simple medical issues, but they struggle with complex ones and minority cases due to limited training data. Two new strategies can help: giving MLVLMs detailed information about what doctors are looking for or teaching a simple learner and having it tell the LVLM what to do. These methods work better on both MIMIC-CXR-JPG and Chexpert datasets, and even help other language models. |
Keywords
» Artificial intelligence » F1 score » Hallucination » Prompting » Question answering
