85 citations · 98 across the 9 of their papers we have counts for
9 papers
ReXInTheWild: A Unified Benchmark for Medical Photograph Understanding
Oishi Banerjee, Sung Eun Kim, Alexandra N. Willauer +9
Everyday photographs taken with ordinary cameras are already widely used in telemedicine and other online health conversations, yet no comprehensive benchmark evaluates whether vis…
CRIMSON: A Clinically-Grounded LLM-Based Metric for Generative Radiology Report Evaluation
Mohammed Baharoon, Thibault Heintz, Siavash Raissi +6
We introduce CRIMSON, a clinically grounded evaluation framework for chest X-ray report generation that assesses reports based on diagnostic correctness, contextual relevance, and…
RadFlag: A Black-Box Hallucination Detection Method for Medical Vision Language Models
Serena Zhang, Sraavya Sambara, Oishi Banerjee +3
Generating accurate radiology reports from medical images is a clinically important but challenging task. While current Vision Language Models (VLMs) show promise, they are prone t…
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…
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…
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…
FineRadScore: A Radiology Report Line-by-Line Evaluation Technique Generating Corrections with Severity Scores
Alyssa Huang, Oishi Banerjee, Kay Wu +2
The current gold standard for evaluating generated chest x-ray (CXR) reports is through radiologist annotations. However, this process can be extremely time-consuming and costly, e…
BenchMD: A Benchmark for Unified Learning on Medical Images and Sensors
Kathryn Wantlin, Chenwei Wu, Shih-Cheng Huang +12
Medical data poses a daunting challenge for AI algorithms: it exists in many different modalities, experiences frequent distribution shifts, and suffers from a scarcity of examples…
CheXtransfer: Performance and Parameter Efficiency of ImageNet Models for Chest X-Ray Interpretation
Alexander Ke, William Ellsworth, Oishi Banerjee +2
Deep learning methods for chest X-ray interpretation typically rely on pretrained models developed for ImageNet. This paradigm assumes that better ImageNet architectures perform be…