Datasets:

liguo12
/

REVA_PO_Datasets

	---
	license: cc-by-4.0
	task_categories:
	- image-to-text
	language:
	- en
	tags:
	- image
	- text
	- medical-imaging
	- radiology
	- chest-x-ray
	- radiology-report-generation
	- reinforcement-learning
	- iu-xray
	- mimic-cxr
	- arxiv:2607.10147
	pretty_name: 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.

	[![Paper](https://img.shields.io/badge/arXiv-2607.10147-b31b1b.svg)](https://arxiv.org/abs/2607.10147)
	[![Code](https://img.shields.io/badge/GitHub-REVA--PO-181717.svg?logo=github)](https://github.com/LiGuo12/REVA_PO)
	[![Weights](https://img.shields.io/badge/Weights-Hugging_Face-FFD21E.svg)](https://huggingface.co/liguo12/REVA_PO_Weights)

	## 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](https://huggingface.co/liguo12/REVA_PO_Weights).

	## Repository Contents

	```text
	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:

	```bash
	pip install -U huggingface_hub
	```

	Download the full repository:

	```python
	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:

	```python
	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/`:

	```bash
	cd REVA_PO_Datasets/iuxray_dataset
	unzip images.zip
	```

	The resulting directory should be:

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

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

	```yaml
	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](https://physionet.org/content/mimic-cxr-jpg/2.1.0/) through PhysioNet, then place its `files/` directory beside the downloaded annotation files:

	```text
	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:

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

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

	```yaml
	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:

	```json
	{
	"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](https://github.com/LiGuo12/REVA_PO) 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](https://creativecommons.org/licenses/by/4.0/). 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](https://github.com/LiGuo12/REVA_PO).
	- 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:

	```bibtex
	@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.