BIOMEDICA

This is the official Hugging Face organization for BIOMEDICA: An Open Biomedical Image-Caption Archive with Vision-Language Models Derived from Scientific Literature (CVPR 2025). Here, you will find large-scale pretraining datasets, large-scale benchmarks, biomedical CLIP models, and small biomedical vision-language models developed as part of the BIOMEDICA project.

BIOMEDICA is maintained by the Stanford MARVL Lab, led by Alejandro Lozano (PhD Candidate at Stanford) and Min Woo Sun, PhD (now at Google DeepMind), under the supervision of Serena Yeung-Levy (P.I.).

Acknowledgments

The Open Biomedical Image-Caption Archive is supported by an NVIDIA academic grant, Amazon Health Grant, and NIH RO1

Citation

If you use these models or datasets in your research, please cite:

@article{sun2025no,
  title={No Tokens Wasted: Leveraging Long Context in Biomedical Vision-Language Models},
  author={Sun, Min Woo and others},
  journal={arXiv preprint arXiv:2510.03978},
  year={2025}
}

@inproceedings{lozano2025biomedica,
  title={Biomedica: An open biomedical image-caption archive, dataset, and vision-language models derived from scientific literature},
  author={Lozano, Alejandro and Sun, Min Woo and Burgess, James and Chen, Liangyu and Nirschl, Jeffrey J and Gu, Jeffrey and Lopez, Ivan and Aklilu, Josiah and Rau, Anita and Katzer, Austin Wolfgang and others},
  booktitle={2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={19724--19735},
  year={2025},
  organization={IEEE}
}

@article{lozano2025large,
  title={A large-scale vision-language dataset derived from open scientific literature to advance biomedical generalist AI},
  author={Lozano, Alejandro and Sun, Min Woo and Burgess, James and Nirschl, Jeffrey J and Polzak, Christopher and Zhang, Yuhui and Chen, Liangyu and Gu, Jeffrey and Lopez, Ivan and Aklilu, Josiah and others},
  journal={arXiv preprint arXiv:2503.22727},
  year={2025}
}