| --- |
| language: |
| - en |
| pretty_name: PubMedVision-Plus |
| task_categories: |
| - image-to-text |
| tags: |
| - medical |
| - biomedical |
| - multimodal |
| - image-captioning |
| - pubmed |
| - huatuogpt |
| configs: |
| - config_name: core |
| data_files: |
| - split: train |
| path: core-*.parquet |
| - config_name: sample |
| default: true |
| data_files: |
| - split: train |
| path: sample/sample.parquet |
| --- |
| |
| <div align="center"> |
| <h1>PubMedVision-Plus</h1> |
| <p><b>A large-scale medical and biomedical image–caption dataset.</b></p> |
| <a href="https://huggingface.co/datasets/FreedomIntelligence/PubMedVision">🤗 PubMedVision</a> |
| </div> |
|
|
| ## ⚡ Introduction |
|
|
| **PubMedVision-Plus** is a large-scale medical image–text dataset built from scientific literature, pairing original figures with expert-authored captions to support medical multimodal pretraining. |
|
|
| - **Medical knowledge at scale.** Drawing on approximately **15M PubMed papers**, the collection is refined through LLM- and VLM-based quality filtering into a **12M high-quality Core**. |
| - **Broad visual coverage.** From radiology and pathology to microscopy, ophthalmology, and endoscopy, the data span diverse medical specialties, with modality and anatomical-region labels retained in the browsing sample. |
|
|
| > **License notice:** Images and captions come from medical journal articles. Please follow the source journals’ image-use and attribution requirements. See the [PMC reuse guidelines](https://pmc.ncbi.nlm.nih.gov/about/copyright/). |
|
|
| ## 📚 Data |
|
|
| Each Core record includes a unique ID, a relative image path, and its corresponding caption. The browsing sample additionally retains modality and anatomical-region labels and embeds the images. |
|
|
| | Data | Image–caption pairs | Construction | |
| | --- | ---: | --- | |
| | **Core** | **12,444,904** | Medical literature collection, followed by LLM/VLM quality filtering. | |
|
|
| > Original figures and author-written captions are preserved without generative rewriting, avoiding hallucinations introduced by synthetic captions. Filtering targets defective images and problematic image–text pairs. |
|
|
| ## 🚀 Usage |
|
|
| ```text |
| PubMedVision-Plus/ |
| ├── README.md |
| ├── core-*.parquet # IDs, captions, and relative image paths |
| ├── images/images-*.tar # Original image archives |
| └── sample/sample.parquet # Examples with embedded images |
| ``` |
|
|
| The Core collection contains 12,444,904 pairs. The browsing sample currently contains 50 examples. |
|
|
| Load the image-embedded sample Parquet from the downloaded repository: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("parquet", data_files="sample/sample.parquet", split="train") |
| image, caption = dataset[0]["image"], dataset[0]["caption"] |
| ``` |
|
|
| For the full Core release, extract all image tar shards **from the repository root** (four archives in parallel): |
|
|
| ```bash |
| find images -maxdepth 1 -type f -name '*.tar' -print0 \ |
| | xargs -0 -P 4 -I {} tar -xf '{}' -C . |
| ``` |
|
|
| Extraction preserves the relative image paths in the Parquet metadata. Load `core-*.parquet` to access IDs, captions, and the corresponding image paths. |
|
|
| ## 📄 Related work |
|
|
| PubMedVision-Plus is introduced in *Incentivizing Medical Vision Capabilities from Large-Scale Multimodal Pre-training* (NeurIPS 2026). If you use this dataset, please cite: |
|
|
| ```bibtex |
| @inproceedings{chen2026incentivizing, |
| title={Incentivizing Medical Vision Capabilities from Large-Scale Multimodal Pre-training}, |
| author={Chen, Junying and Cai, Zhenyang and Yang, Yunjin and Cai, Kunyuan and Wang, Rongsheng and Wang, Xidong and Yu, Geyang and Feng, Xiangyi and Wang, Benyou}, |
| booktitle={The Fortieth Annual Conference on Neural Information Processing Systems}, |
| year={2026} |
| } |
| ``` |
|
|
|
|
