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The TCGA-UniformTumor-8K dataset is released under the CC-BY-NC-ND 4.0 license and may only be used for non-commercial, academic research purposes with proper attribution. Any commercial use, sale, or other monetization of the dataset and their derivatives are prohibited and requires prior approval. Please note that the primary email used to sign up for your Hugging Face account must match your institutional email to receive approval. By downloading the features, you attest that all information (affiliation, research use) is correct and up-to-date. Downloading the dataset requires prior registration on Hugging Face and agreeing to the terms of use. By downloading the dataset, you agree not to distribute, publish or reproduce a copy of the data. If another user within your organization wishes to use the data, they must register as an individual user and agree to comply with the terms of use. If you are a commercial entity, please contact the corresponding author.
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Dataset Card for TCGA-UniformTumor-8K
What is TCGA-UniformTumor-8K?
TCGA-UniformTumor-8K dataset is a region-level pan-cancer subtyping resource comprising 25,495 ROIs of 8,192 × 8,192 pixels. These regions were extracted from 9,662 H&E-stained FFPE diagnostic histopathology WSIs sourced from TCGA. The tumor regions were manually annotated by two expert pathologists, with slide exclusion due to poor staining, poor focus, lacking cancerous regions and incorrect cancer types. Approximately three representative tumor regions per WSI were annotated with pixel-level contours. For each contour, we center-cropped an image region of 8,192 × 8,192 pixels to encompass both the dense tumor and its surrounding tissue context.
Instructions for Downloading TCGA-UniformTumor-8K
Install huggingface-hub and datasets, and log into your account
pip install huggingface-hub
pip install datasets
from huggingface_hub import login
login(token="YOUR HUGGINGFACE TOKEN")
Download TCGA-UniformTumor-8K
from datasets import load_dataset
ds = load_dataset("MahmoodLab/TCGA-UniformTumor-8K") # returns a DatasetDict
# example:
img, row = ds["train"][0]["image"], ds["train"][0] # PIL image + image metadata
How to cite:
@article{ding2025multimodal,
title={A multimodal whole-slide foundation model for pathology},
author={Ding, Tong and Wagner, Sophia J and Song, Andrew H and Chen, Richard J and Lu, Ming Y and Zhang, Andrew and Vaidya, Anurag J and Jaume, Guillaume and Shaban, Muhammad and Kim, Ahrong and others},
journal={Nature Medicine},
pages={1--13},
year={2025},
publisher={Nature Publishing Group US New York}
}
Contact:
- Tong Ding (
tong_ding@g.harvard.edu) - Sophia J. Wagner (
sjwagner@bwh.harvard.edu) - Andrew H. Song (
asong@bwh.harvard.edu) - Richard J. Chen (
richardchen@g.harvard.edu) - Faisal Mahmood (
faisalmahmood@bwh.harvard.edu)
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