liguo12/REVA_PO_Datasets

REVA-PO Datasets

Dataset files and annotations used in REVA-PO: Stabilizing Reinforcement Learning for Chest X-ray Report Generation, accepted to ECCV 2026.

Overview

REVA-PO is a reinforcement learning framework for chest X-ray report generation. It uses Response-Weighted Regularization and Validation-Anchored Policy Reset to improve training stability and exploration.

This repository contains the IU-Xray data package and the MIMIC-CXR annotation files used by the released code. The model checkpoints are available in the REVA-PO weights repository.

Repository Contents

REVA_PO_Datasets/
├── iuxray_dataset/
│   ├── annotation_with_categories.json
│   └── images.zip
└── mimic_dataset/
    ├── mimic_with_categories.json
    └── mimic_with_categories_sampled_10k.json

Path	Description
iuxray_dataset/images.zip	IU-Xray chest X-ray images arranged for the released REVA-PO data loader.
iuxray_dataset/annotation_with_categories.json	IU-Xray train, validation, and test annotations with report text and clinical categories.
mimic_dataset/mimic_with_categories.json	Full MIMIC-CXR annotation file used for supervised training.
mimic_dataset/mimic_with_categories_sampled_10k.json	MIMIC-CXR annotation file with 9,974 randomly sampled training instances for reinforcement learning. The validation and test splits are unchanged.

Download

Install the Hugging Face Hub client:

pip install -U huggingface_hub

Download the full repository:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="liguo12/REVA_PO_Datasets",
    repo_type="dataset",
    local_dir="REVA_PO_Datasets",
)

To download only selected files:

from huggingface_hub import hf_hub_download

iu_annotations = hf_hub_download(
    repo_id="liguo12/REVA_PO_Datasets",
    repo_type="dataset",
    filename="iuxray_dataset/annotation_with_categories.json",
)

mimic_annotations = hf_hub_download(
    repo_id="liguo12/REVA_PO_Datasets",
    repo_type="dataset",
    filename="mimic_dataset/mimic_with_categories_sampled_10k.json",
)

IU-Xray Setup

Extract images.zip inside iuxray_dataset/:

cd REVA_PO_Datasets/iuxray_dataset
unzip images.zip

The resulting directory should be:

iuxray_dataset/
├── images/
│   ├── CXR2384_IM-0942/
│   │   ├── 0.png
│   │   └── 1.png
│   └── ...
└── annotation_with_categories.json

Set the following fields in the REVA-PO configuration files:

storage: /path/to/iuxray_dataset
ann_file: /path/to/iuxray_dataset/annotation_with_categories.json

MIMIC-CXR Setup

MIMIC-CXR images are not included in this repository. Obtain authorized access to MIMIC-CXR-JPG v2.1.0 through PhysioNet, then place its files/ directory beside the downloaded annotation files:

mimic_dataset/
├── files/
│   ├── p10/
│   │   ├── p10000032/
│   │   │   ├── s50414267/
│   │   │   │   ├── 02aa804e-bde0afdd-112c0b34-7bc16630-4e384014.jpg
│   │   │   │   └── ...
│   │   │   └── ...
│   │   └── ...
│   └── ...
├── mimic_with_categories.json
└── mimic_with_categories_sampled_10k.json

For supervised training (stage 1 and stage 2), use:

storage: /path/to/mimic_dataset
ann_file: /path/to/mimic_dataset/mimic_with_categories.json

For the released reinforcement learning (stage 3), use:

storage: /path/to/mimic_dataset
ann_file: /path/to/mimic_dataset/mimic_with_categories_sampled_10k.json

IU-Xray Annotation Format

The annotation files are JSON objects organized by data split:

{
  "train": [
    {
      "id": "CXR2384_IM-0942",
      "report": "The heart size and pulmonary vascularity appear within normal limits...",
      "image_path": [
        "CXR2384_IM-0942/0.png",
        "CXR2384_IM-0942/1.png"
      ],
      "split": "train",
      "positive_categories": ["No Finding"],
      "uncertain_categories": [],
      "negative_categories": [
        "Cardiomegaly",
        "Lung Opacity",
        "Pneumothorax",
        "Pleural Effusion"
      ],
      "categories": [
        "No Finding"
      ]
    }
  ],
  "val": [...],
  "test": [...]
}

Common fields include:

Field	Description
id	Study or examination identifier.
report	Reference radiology report.
image_path	Relative path or paths to the associated chest X-ray images.
split	Data split.
positive_categories	Clinical categories labeled as positive.
uncertain_categories	Clinical categories labeled as uncertain.
negative_categories	Clinical categories labeled as negative.
categories	Categories used by the REVA-PO training pipeline.

The exact fields can differ slightly between IU-Xray and MIMIC-CXR. Use the released data loaders in the code repository as the reference implementation.

Intended Use

These files are intended for research on:

• Chest X-ray report generation
• Medical image-to-text generation
• Clinical vision-language learning
• Reinforcement learning for report generation
• Evaluation of linguistic quality and clinical accuracy

They are not intended for direct clinical diagnosis, treatment decisions, or use without independent medical validation.

Limitations

The annotations inherit the limitations of IU-Xray and MIMIC-CXR, including dataset-specific reporting patterns, label noise, class imbalance, and differences between institutions. Performance measured on these datasets does not establish safety or effectiveness in clinical use.

License and Access Notes

The annotation files and other original materials released in this repository are licensed under the Creative Commons Attribution 4.0 International License. You may use, share, and adapt these materials for research or other purposes, provided that appropriate credit is given to the REVA-PO authors.

This license applies only to materials created and released by the REVA-PO authors. It does not replace the terms of the original datasets:

• MIMIC-CXR-JPG requires credentialed access through PhysioNet. Users must follow the PhysioNet Credentialed Health Data Use Agreement and the MIMIC-CXR-JPG access conditions.
• IU-Xray data remains subject to the terms of its original source.
• The REVA-PO source code is released under the license stated in the GitHub repository.
• Users are responsible for checking and following all terms that apply to the source datasets.

Citation

If you use these files or the REVA-PO method, please cite:

@misc{guo2026revapo,
  title        = {REVA-PO: Stabilizing Reinforcement Learning for Chest X-ray Report Generation},
  author       = {Li Guo and Anas M. Tahir and Z. Jane Wang},
  year         = {2026},
  eprint       = {2607.10147},
  archivePrefix = {arXiv},
  primaryClass = {cs.CV},
  url          = {https://arxiv.org/abs/2607.10147}
}

Please also cite the original IU-Xray and MIMIC-CXR resources when applicable.