Uncovering Knowledge Gaps in Radiology Report Generation Models through Knowledge Graphs

X Zhang, JN Acosta, HY Zhou, P Rajpurkar - arxiv preprint arxiv …, 2024 - arxiv.org
Recent advancements in artificial intelligence have significantly improved the automatic
generation of radiology reports. However, existing evaluation methods fail to reveal the …

ReXrank: A Public Leaderboard for AI-Powered Radiology Report Generation

X Zhang, HY Zhou, X Yang, O Banerjee… - arxiv preprint arxiv …, 2024 - arxiv.org
AI-driven models have demonstrated significant potential in automating radiology report
generation for chest X-rays. However, there is no standardized benchmark for objectively …

HeadCT-ONE: Enabling Granular and Controllable Automated Evaluation of Head CT Radiology Report Generation

JN Acosta, X Zhang, S Dogra, HY Zhou… - arxiv preprint arxiv …, 2024 - arxiv.org
We present Head CT Ontology Normalized Evaluation (HeadCT-ONE), a metric for
evaluating head CT report generation through ontology-normalized entity and relation …

ER2Score: LLM-based Explainable and Customizable Metric for Assessing Radiology Reports with Reward-Control Loss

Y Liu, Y Li, Z Wang, X Liang, L Liu, L Wang… - arxiv preprint arxiv …, 2024 - arxiv.org
Automated radiology report generation (R2Gen) has advanced significantly, introducing
challenges in accurate evaluation due to its complexity. Traditional metrics often fall short by …