4 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2024
ReXamine-Global: A Framework for Uncovering Inconsistencies in Radiology Report Generation Metrics
Oishi Banerjee, Agustina Saenz, Kay Wu +17
Given the rapidly expanding capabilities of generative AI models for radiology, there is a need for robust metrics that can accurately measure the quality of AI-generated radiology…
cs.AI2023
Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting
Benjamin Yan, Ruochen Liu, David E. Kuo +8
Automatically generated reports from medical images promise to improve the workflow of radiologists. Existing methods consider an image-to-report modeling task by directly generati…
cs.CL2023★ 4 cited
RadGraph2: Modeling Disease Progression in Radiology Reports via Hierarchical Information Extraction
Sameer Khanna, Adam Dejl, Kibo Yoon +4
We present RadGraph2, a novel dataset for extracting information from radiology reports that focuses on capturing changes in disease state and device placement over time. We introd…