7 papers
Reinforcement Learning Improves LLM Accuracy and Reasoning in Disease Classification from Radiology Reports
Yishu Wei, Yi Lin, Adam Flanders +2
Accurate disease classification from radiology reports is essential for many applications. While supervised fine-tuning (SFT) of lightweight LLMs improves accuracy, it can degrade…
CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification
Hexin Dong, Yi Lin, Pengyu Zhou +25
Chest X-ray (CXR) interpretation is hindered by the long-tailed distribution of pathologies and the open-world nature of clinical environments. Existing benchmarks often rely on cl…
RSNA Large Language Model Benchmark Dataset for Chest Radiographs of Cardiothoracic Disease: Radiologist Evaluation and Validation Enhanced by AI Labels (REVEAL-CXR)
Yishu Wei, Adam E. Flanders, Errol Colak +35
Multimodal large language models have demonstrated comparable performance to that of radiology trainees on multiple-choice board-style exams. However, to develop clinically useful…
A Multi-agent Large Language Model Framework to Automatically Assess Performance of a Clinical AI Triage Tool
Adam E. Flanders, Yifan Peng, Luciano Prevedello +6
Purpose: The purpose of this study was to determine if an ensemble of multiple LLM agents could be used collectively to provide a more reliable assessment of a pixel-based AI triag…
The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset
Tyler J. Richards, Adam E. Flanders, Errol Colak +33
The Radiological Society of North America (RSNA) Lumbar Degenerative Imaging Spine Classification (LumbarDISC) dataset is the largest publicly available dataset of adult MRI lumbar…
CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray
Mingquan Lin, Gregory Holste, Song Wang +30
The CXR-LT series is a community-driven initiative designed to enhance lung disease classification using chest X-rays (CXR). It tackles challenges in open long-tailed lung disease…