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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.LG2024★ 1 cited
Direct Preference Optimization for Suppressing Hallucinated Prior Exams in Radiology Report Generation
Oishi Banerjee, Hong-Yu Zhou, Subathra Adithan +3
Recent advances in generative vision-language models (VLMs) have exciting potential implications for AI in radiology, yet VLMs are also known to produce hallucinations, nonsensical…