2 citations · 6 across the 16 of their papers we have counts for
5 papers · 1 filter
FactCheXcker: Mitigating Measurement Hallucinations in Chest X-ray Report Generation Models
Alice Heiman, Xiaoman Zhang, Emma Chen +2
Medical vision-language models often struggle with generating accurate quantitative measurements in radiology reports, leading to hallucinations that undermine clinical reliability…
ReXrank: A Public Leaderboard for AI-Powered Radiology Report Generation
Xiaoman Zhang, Hong-Yu Zhou, Xiaoli Yang +6
AI-driven models have demonstrated significant potential in automating radiology report generation for chest X-rays. However, there is no standardized benchmark for objectively eva…
ReXplain: Translating Radiology into Patient-Friendly Video Reports
Luyang Luo, Jenanan Vairavamurthy, Xiaoman Zhang +7
Radiology reports, designed for efficient communication between medical experts, often remain incomprehensible to patients. This inaccessibility could potentially lead to anxiety,…
HeadCT-ONE: Enabling Granular and Controllable Automated Evaluation of Head CT Radiology Report Generation
Julián N. Acosta, Xiaoman Zhang, Siddhant Dogra +5
We present Head CT Ontology Normalized Evaluation (HeadCT-ONE), a metric for evaluating head CT report generation through ontology-normalized entity and relation extraction. HeadCT…
Uncovering Knowledge Gaps in Radiology Report Generation Models through Knowledge Graphs
Xiaoman Zhang, Julián N. Acosta, Hong-Yu Zhou +1
Recent advancements in artificial intelligence have significantly improved the automatic generation of radiology reports. However, existing evaluation methods fail to reveal the mo…