microsoft/BiomedParse
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BiomedParseData
This is the official dataset repository for "A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities".
[Code] [Paper] [Demo] [Model] [Data]
We processed from the below public segmentation datasets, and host a subset of our processed datasets as ZIP files here. Each instance include a 1024x1024 PNG image, a list of textual description for the segmentation target, and a binary groundtruth mask also in 1024x1024 PNG.
You are welcome to use any subset of the datasets to train or evaluate BiomedParse, as well as develop your new model. Please cite our paper and the original dataset that you used.
Zhao, T., Gu, Y., Yang, J. et al. A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities. Nat Methods (2024). https://doi.org/10.1038/s41592-024-02499-w
@article{zhao2024biomedparse,
title = {A foundation model for joint segmentation, detection, and recognition of biomedical objects across nine modalities},
author = {Zhao, Theodore and Gu, Yu and Yang, Jianwei and Usuyama, Naoto and Lee, Ho Hin and Kiblawi, Sid and Naumann, Tristan and Gao, Jianfeng and Crabtree, Angela and Abel, Jacob and Moung-Wen, Christine and Piening, Brian and Bifulco, Carlo and Wei, Mu and Poon, Hoifung and Wang, Sheng},
journal = {Nature Methods},
year = {2024},
publisher = {Nature Publishing Group UK London},
url = {https://www.nature.com/articles/s41592-024-02499-w},
doi = {10.1038/s41592-024-02499-w}
}
BiomedParseData was created from preprocessing publicly available biomedical image segmentation datasets. These datasets are provided pre-formatted for convenience. For additional information about the datasets or their licenses, please reach out to the owners:
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Models trained or fine-tuned on microsoft/BiomedParseData