README.md

metadata

license: cc-by-nc-nd-4.0
extra_gated_prompt: >-
  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.
extra_gated_fields:
  Full name (first and last): text
  Current affiliation (no abbreviations): text
  Type of Affiliation:
    type: select
    options:
      - Academia
      - Industry
      - label: Other
        value: other
  Current and official institutional email (**this must match your primary email in your Hugging Face account, @gmail/@hotmail/@qq email domains will be denied**): text
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  I agree not to distribute the model, if another user within your organization wishes to use the released pretrained features, they must register as an individual user: checkbox
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: val
        path: data/val-*
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: PATIENT
      dtype: string
    - name: FILENAME
      dtype: string
    - name: image
      dtype: image
    - name: x
      dtype: int64
    - name: 'y'
      dtype: int64
    - name: cancer
      dtype: string
    - name: source_site
      dtype: string
    - name: source
      dtype: string
  splits:
    - name: train
      num_bytes: 186375931310.701
      num_examples: 13853
    - name: val
      num_bytes: 49282909947.13
      num_examples: 3434
    - name: test
      num_bytes: 118463491347.128
      num_examples: 8208
  download_size: 376933091793
  dataset_size: 354122332604.959

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)