Summary of Gemex: a Large-scale, Groundable, and Explainable Medical Vqa Benchmark For Chest X-ray Diagnosis, by Bo Liu et al.
GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis
by Bo Liu, Ke Zou, Liming Zhan, Zexin Lu, Xiaoyu Dong, Yidi Chen, Chengqiang Xie, Jiannong Cao, Xiao-Ming Wu, Huazhu Fu
First submitted to arxiv on: 25 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 Medical Visual Question Answering (VQA) integrates computer vision and natural language processing to respond to clinical inquiries about medical images. The current medical VQA datasets lack visual and textual explanations for answers, restricting their ability to satisfy the comprehension needs of patients and junior doctors. Additionally, they often offer a narrow range of question formats, inadequately reflecting diverse clinical requirements. To address these challenges, researchers introduce a large-scale, Groundable, and Explainable Medical VQA benchmark (GEMeX) featuring multi-modal explainability mechanisms and four distinct question types. The GEMeX dataset is evaluated with 10 representative large vision language models, which underperform initially but improve significantly after fine-tuning. This development demonstrates the effectiveness of the dataset in improving performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical Visual Question Answering (VQA) helps computers understand medical images and answer questions about them. Right now, these datasets have two big problems: they don’t explain why their answers are correct, and they only ask simple questions that aren’t like what doctors would ask. To fix this, scientists created a huge dataset with many types of questions and explanations for the answers. They tested it with 10 big computer models and found that they didn’t do very well at first but got much better after practicing on the new data. |
Keywords
» Artificial intelligence » Fine tuning » Multi modal » Natural language processing » Question answering
