LLaVA-Rad MIMIC-CXR Annotations

**LLaVA-Rad MIMIC-CXR** features more accurate section extractions from MIMIC-
CXR free-text radiology reports. Traditionally, rule-based methods were used
to extract sections such as the reason for exam, findings, and impression.
However, these approaches often fail due to inconsistencies in report
structure and clinical language. In this work, we leverage GPT-4 to extract
these sections more reliably, adding 237,073 image-text pairs to the training
split and 1,952 pairs to the validation split. This enhancement afforded the
development and fine-tuning of LLaVA-Rad, a multimodal large language model
(LLM) tailored for radiology applications, achieving improved performance on
report generation tasks.

This resource is provided to support reproducibility and for the benefit of
the research community, enabling further exploration in vision-language
modeling. For more details, please refer to the accompanying paper [1].

**LLaVA-Rad MIMIC-CXR** 可从MIMIC-CXR自由文本放射科报告中实现更精准的章节提取。传统上，研究人员多采用基于规则的方法，从报告中提取检查指征、影像所见、诊断印象等章节。然而，由于报告结构与临床语言存在不一致性，这类方法往往难以达到理想效果。本研究依托GPT-4实现了更可靠的章节提取，为训练集新增237073组图文对，为验证集新增1952组图文对。通过这一优化，团队得以开发并微调面向放射学应用的多模态大语言模型（LLM）LLaVA-Rad，使其在报告生成任务上的性能得到显著提升。

本数据集旨在支持研究可复现性，服务于科研社区，为视觉语言建模领域的进一步探索提供支撑。欲了解更多细节，请参阅随附的参考文献[1]。

搜集汇总

背景与挑战

背景概述

LLaVA-Rad MIMIC-CXR Annotations是一个基于MIMIC-CXR放射学报告的标注数据集，通过GPT-4技术自动提取报告中的检查原因、发现和印象等章节，解决了传统规则方法在处理结构不一致和临床语言时的局限性。该数据集包含超过23万个图像-文本对，以JSON格式组织，专门用于训练和微调LLaVA-Rad多模态大语言模型，以提升放射学报告生成任务的性能。