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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:

Dataset URL
amos22 https://amos22.grand-challenge.org/
MSD (Medical Segmentation Decathlon) http://medicaldecathlon.com/
KiTS23 https://github.com/neheller/kits23
BTCV https://www.synapse.org/#!Synapse:syn3193805/wiki/217790
COVID-19 CT https://www.kaggle.com/datasets/andrewmvd/covid19-ct-scans
LIDR-IDRI https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI
ACDC https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html
M&Ms https://www.ub.edu/mnms/
PROMISE12 cite https://doi.org/10.1016/j.media.2013.12.002
LGG https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation
COVID-QU-Ex https://www.kaggle.com/datasets/anasmohammedtahir/covidqu
QaTa-COV19 https://www.kaggle.com/datasets/aysendegerli/qatacov19-dataset
SIIM-ACR Pneumothorax Segmentation https://www.kaggle.com/datasets/vbookshelf/pneumothorax-chest-xray-images-and-masks
Chest Xray Masks and Labels Dataset https://datasetninja.com/chest-xray
COVID-19 Radiography Database https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database
CAMUS https://www.creatis.insa-lyon.fr/Challenge/camus/index.html
BUSI https://scholar.cu.edu.eg/?q=afahmy/pages/dataset
FH-PS-AOP https://zenodo.org/records/7851339#.ZEH6eHZBztU
CDD-CESM https://www.cancerimagingarchive.net/collection/cdd-cesm/
PolypGen https://www.synapse.org/#!Synapse:syn26376615/wiki/613312
NeoPolyp https://www.kaggle.com/c/bkai-igh-neopolyp/data
ISIC 2018 https://challenge2018.isic-archive.com/task1/
UwaterlooSkinCancer Skin Cancer Detection | Vision and Image Processing Lab | University of Waterloo
OCT-CME https://www.kaggle.com/datasets/zeeshanahmed13/intraretinal-cystoid-fluid
REFUGE https://bitbucket.org/woalsdnd/refuge/src
G1020 https://www.dfki.uni-kl.de/g1020
DRIVE https://drive.grand-challenge.org/
GlaS https://warwick.ac.uk/fac/cross_fac/tia/data/glascontest/
PanNuke https://jgamper.github.io/PanNukeDataset/
FUMPE https://figshare.com/collections/FUMPE/4107803/1
TotalSegmentator https://github.com/wasserth/TotalSegmentator
BraTS2023 https://www.synapse.org/#!Synapse:syn51156910/wiki/621282
AbdomenCT-1K https://github.com/JunMa11/AbdomenCT-1K
US Simulation & Segmentation https://www.kaggle.com/datasets/ignaciorlando/ussimandsegm
CDD-CESM https://www.cancerimagingarchive.net/collection/cdd-cesm/
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Models trained or fine-tuned on microsoft/BiomedParseData