---
license: cc-by-nc-4.0
task_categories:
  - visual-question-answering
language:
  - en
pretty_name: MedRCube
tags:
  - medical
  - multimodal
  - benchmark
  - radiology
size_categories:
  - 1K<n<10K
---

# MedRCube

<p align="center">
  <a href="https://arxiv.org/abs/2604.13756" target="_blank">📖 arXiv Paper</a> •
  <a href="https://github.com/F1mc/MedRCube" target="_blank">💻 GitHub</a>
</p>

## Overview

**MedRCube** is a multidimensional medical imaging benchmark designed to answer not just *how well* a model performs, but *where*, *why*, and *how credibly* it does so.

It comprises **7,626** rigorously constructed samples from **36** datasets, spanning **5** anatomical regions (Heart, Chest, Breast, Lung, Brain), **4** imaging modalities (X-ray, CT, MRI, Ultrasound), and **8** cognitive tasks organized into a three-tier hierarchy, built through a systematic pipeline with **radiologist and clinical expert** participation throughout.

Every sample is mapped into a structured **Competency Space** defined by three orthogonal axes (Anatomy × Modality × Task). Each intersection forms a **Competency Voxel** for precise capability localization. By constructing multi-level task chains on the same image, MedRCube further enables **reasoning credibility verification** — checking whether a model's correct diagnosis is genuinely supported by correct perception, or merely a lucky guess.

## Data Fields

Each record in `test.json` contains:

| Field | Description |
|---|---|
| `id`, `dataset` | Sample identifier and source dataset name |
| `image_path` | Relative path to the image (if present) |
| `question` | The question text |
| `option_A` / `option_B` / `option_C` / `option_D` | answer choices |
| `gt_answer`, `correct_index` | Ground-truth answer and its index |
| `task`, `modality`, `parts` | Competency Space coordinates |
| `original_task` | Original task in source dataset |
| `restricted` | `true` if images cannot be redistributed directly |

## Loading with `datasets`

```python
from datasets import load_dataset

ds = load_dataset("Flmc/MedRCube", split="test")
print(ds[0])
```

If an example is restricted (or the image file does not exist), its `image` field will be `None`.

## Download for Evaluation

To use with the [GitHub evaluation scripts](https://github.com/F1mc/MedRCube), download the full snapshot:

```bash
huggingface-cli download YOUR_HF_ORG/MedRCube \
  --repo-type dataset \
  --local-dir ./MedRCube \
  --local-dir-use-symlinks False
```

Then point the evaluator's `--dataset_path` to the snapshot root.

## Restricted Sources

Some sources cannot redistribute images. We release the questions now, and will provide reproducible preprocessing scripts (**coming soon**) so researchers can reconstruct images after obtaining access from the original providers. See `restricted_sources.json` for the full list.

## License

This dataset is released under [CC-BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). The evaluation code is licensed under [Apache-2.0](https://github.com/F1mc/MedRCube/blob/main/LICENSE).

## Citation

If you find MedRCube helpful, please cite:

```bibtex
@misc{medrcube2026,
  title={MedRCube: A Multidimensional Framework for Fine-Grained and In-Depth Evaluation of MLLMs in Medical Imaging},
  author={Bao, Zhijie and Chen, Fangke and Bao, Licheng and Zhang, Chenhui and Chen, Wei and Peng, Jiajie and Wei, Zhongyu},
  journal={arXiv preprint},
  year={2026},
  eprint={2604.13756},
  url={https://arxiv.org/abs/2604.13756},
}
```