most citedReFINE: A Reward-Based Framework for Interpretable and Nuanced Evaluation of Radiology Report Generation

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2025

A Review of Longitudinal Radiology Report Generation: Dataset Composition, Methods, and Performance Evaluation

Shaoyang Zhou, Yingshu Li, Yunyi Liu +3

Chest Xray imaging is a widely used diagnostic tool in modern medicine, and its high utilization creates substantial workloads for radiologists. To alleviate this burden, vision la…

cs.CL2025

RadReason: Radiology Report Evaluation Metric with Reasons and Sub-Scores

Yingshu Li, Yunyi Liu, Lingqiao Liu +2

Evaluating automatically generated radiology reports remains a fundamental challenge due to the lack of clinically grounded, interpretable, and fine-grained metrics. Existing metho…

cs.CV2025

S-RRG-Bench: Structured Radiology Report Generation with Fine-Grained Evaluation Framework

Yingshu Li, Yunyi Liu, Zhanyu Wang +4

Radiology report generation (RRG) for diagnostic images, such as chest X-rays, plays a pivotal role in both clinical practice and AI. Traditional free-text reports suffer from redu…

cs.CL20241 cited

ReFINE: A Reward-Based Framework for Interpretable and Nuanced Evaluation of Radiology Report Generation

Yunyi Liu, Yingshu Li, Zhanyu Wang +4

Automated radiology report generation (R2Gen) has advanced significantly, introducing challenges in accurate evaluation due to its complexity. Traditional metrics often fall short…

cs.CV2024

KARGEN: Knowledge-enhanced Automated Radiology Report Generation Using Large Language Models

Yingshu Li, Zhanyu Wang, Yunyi Liu +3

Harnessing the robust capabilities of Large Language Models (LLMs) for narrative generation, logical reasoning, and common-sense knowledge integration, this study delves into utili…