| --- |
| 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}, |
| } |
| ``` |
|
|
