Summary of Refine: a Reward-based Framework For Interpretable and Nuanced Evaluation Of Radiology Report Generation, by Yunyi Liu et al.
ReFINE: A Reward-Based Framework for Interpretable and Nuanced Evaluation of Radiology Report Generation
by Yunyi Liu, Yingshu Li, Zhanyu Wang, Xinyu Liang, Lingqiao Liu, Lei Wang, Luping Zhou
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 Automated radiology report generation (R2Gen) has become increasingly complex, making traditional evaluation metrics insufficient. To address this issue, we introduce ReFINE, a novel automatic evaluation metric designed specifically for R2Gen. Our approach utilizes a reward model and a margin-based reward enforcement loss to score reports according to user-defined criteria. Additionally, it provides detailed sub-scores, enhancing interpretability. We leverage GPT-4 to generate extensive training data using two distinct scoring systems, each containing reports of varying quality with corresponding scores. Our experiments demonstrate ReFINE’s high correlation with human judgments and superior performance in model selection compared to traditional metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a computer program that can read medical reports written by doctors. This report is about creating an easy way for computers to understand these reports and decide if they are good or bad. The problem is that current methods are not very accurate, so we need a new approach. Our solution is called ReFINE, which uses a special type of training data and a reward system to evaluate the quality of medical reports. We tested ReFINE and found it works much better than traditional methods. This means that computers can now help doctors by reading and understanding their reports more accurately. |
Keywords
» Artificial intelligence » Gpt
