4 citations · 12 across the 11 of their papers we have counts for
11 papers
Graph-Supervised Hierarchical Clinical Alignment for Radiology Report Generation with Large Language Models
Yingshu Li, Yunyi Liu, Zhanyu Wang +4
Radiology report generation (RRG) has recently benefited from large language models, which substantially improve report fluency. However, clinically faithful generation remains cha…
DRRG: A Discrete Diffusion Framework for Radiology Report Generation
Shaoyang Zhoua, Yingshu Li, Yunyi Liu +4
Purpose: Automatic radiology report generation (RRG) has been widely explored to improve reporting accuracy and reduce radiologists' workload. Most existing methods rely on autoreg…
Seeing What Matters: Lesion-Aware High-Resolution Patch Discovery and Fusion for Chest X-ray Report Generation
Yingshu Li, Yunyi Liu, Zhenghao Chen +5
Despite rapid advances in chest X-ray (CXR) foundation models, most radiology report generation (RRG) systems still rely on heavily downsampled inputs (e.g., 256x256) due to the fi…
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…
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…
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…
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…
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…
MRScore: Evaluating Radiology Report Generation with LLM-based Reward System
Yunyi Liu, Zhanyu Wang, Yingshu Li +4
In recent years, automated radiology report generation has experienced significant growth. This paper introduces MRScore, an automatic evaluation metric tailored for radiology repo…
MedXChat: A Unified Multimodal Large Language Model Framework towards CXRs Understanding and Generation
Ling Yang, Zhanyu Wang, Zhenghao Chen +2
Multimodal Large Language Models (MLLMs) have shown success in various general image processing tasks, yet their application in medical imaging is nascent, lacking tailored models.…
A Systematic Evaluation of GPT-4V's Multimodal Capability for Medical Image Analysis
Yingshu Li, Yunyi Liu, Zhanyu Wang +7
This work conducts an evaluation of GPT-4V's multimodal capability for medical image analysis, with a focus on three representative tasks of radiology report generation, medical vi…