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].