CoolFace
Datasetpublic

Yamini-1628/MIMIC-CXR-RRG

MIMIC-CXR-RRG: Radiology Report Generation Subsets This dataset provides two carefully filtered and structured subsets from the MIMIC-CXR dataset, specifically designed for Radiology Report Generation (RRG) tasks. It includes image-report pairs focused on the Findings and Impression sections, targeting frontal-view chest X-rays only. 📚 Dataset Overview Subset Section Target Split #Samples View Type findings_section Findings test 2461 Frontal only… See the full description on the dataset page: https://huggingface.co/datasets/Yamini-1628/MIMIC-CXR-RRG.

sourceHugging Faceupdated 9mo agoView on Hugging Face
3likes131downloads
Dataset Card

MIMIC-CXR-RRG: Radiology Report Generation Subsets

This dataset provides two carefully filtered and structured subsets from the MIMIC-CXR dataset, specifically designed for Radiology Report Generation (RRG) tasks. It includes image-report pairs focused on the Findings and Impression sections, targeting frontal-view chest X-rays only.

📚 Dataset Overview

SubsetSection TargetSplit#SamplesView Type
findings_sectionFindingstest2461Frontal only
impression_sectionImpressiontest2343Frontal only
  • —The splits follow the evaluation protocol used in models such as Libra and MAIRA-2.
  • —Images and labels are provided in a test-only setting, useful for benchmarking and zero-shot evaluation.

🧾 Data Format

Each instance in both subsets contains:

  • —📷 main_image – The frontal-view chest X-ray
  • —📷 prior_image – (Optional) Prior image if available
  • —📝 Text sections:
  • —findings_section
  • —impression_section
  • —indication_section
  • —comparison_section
  • —technique_section
  • —history_section
  • —examination_section
  • —💬 default_prompt – Prompt for generation tasks
  • —🧾 Metadata:
  • —dicom_id, study_id, subject_id
  • —Acquisition info: Rows, Columns, StudyDate, ViewPosition, etc.

🚀 How to Use

python
from datasets import load_dataset

# Load a specific subset (e.g., findings_section)
ds = load_dataset("X-iZhang/MIMIC-CXR-RRG", name="findings_section", split="test")

# Display an image
from PIL import Image
ds[0]["main_image"].show()

# View sample
print(ds[0]["findings_section"])

✏️ Citation

@misc{zhang2025ccdmitigatinghallucinationsradiology,
      title={CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decoding}, 
      author={Xi Zhang and Zaiqiao Meng and Jake Lever and Edmond S. L. Ho},
      year={2025},
      eprint={2509.23379},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.23379}, 
}

and

@misc{zhang2025libraleveragingtemporalimages,
      title={Libra: Leveraging Temporal Images for Biomedical Radiology Analysis}, 
      author={Xi Zhang and Zaiqiao Meng and Jake Lever and Edmond S. L. Ho},
      year={2025},
      eprint={2411.19378},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2411.19378}, 
}