Summary of Tri-vqa: Triangular Reasoning Medical Visual Question Answering For Multi-attribute Analysis, by Lin Fan et al.
Tri-VQA: Triangular Reasoning Medical Visual Question Answering for Multi-Attribute Analysis
by Lin Fan, Xun Gong, Cenyang Zheng, Yafei Ou
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 A novel framework, Triangular Reasoning VQA (Tri-VQA), is proposed to construct a more cohesive and stable Med-VQA structure. This approach addresses the limitation of existing joint embedding-based methods by providing reverse causal questions that elucidate the source of the answer and stimulate more reasonable forward reasoning processes. The method is evaluated on the Endoscopic Ultrasound (EUS) multi-attribute annotated dataset from five centers, demonstrating its superiority over existing approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical Visual Question Answering (Med-VQA) helps patients engage with their health and clinical experts provide second opinions. However, current Med-VQA methods don’t explain how they get their answers. This makes it hard to trust the results. To fix this, a new approach called Triangular Reasoning VQA (Tri-VQA) is developed. It asks “why” questions to understand where the answer comes from and makes better predictions. The method works well on a specific medical dataset and outperforms other approaches. |
Keywords
» Artificial intelligence » Embedding » Question answering
