Histology images from uniform tumor regions in TCGA Whole Slide Images (TCGA-UT)

TCGA-UT Dataset Documentation 

Quick Links

Dataset on Hugging Face: For users interested in benchmarking foundation models or feature extractors, please visit TCGA-UT on Hugging Face

Original Paper: Universal encoding of pan-cancer histology by deep texture representations

Dataset Overview

The TCGA-UT dataset is a large-scale collection of histopathological image patches from human cancer tissues. It contains 1,608,060 image patches extracted from hematoxylin & eosin (H&E) stained histological samples across 32 different types of solid cancers.

Key Features

Size: Over 1.6 million image patches

Resolution: All patches are standardized to 256 x 256 pixels

Source: Derived from The Cancer Genome Atlas (TCGA) dataset

Quality: Curated by trained pathologists

Coverage: 32 different cancer types

Patient Base: 7,175 patients from 8,736 diagnostic slides

Data Collection Process

Image Source: Whole Slide Images (WSI) were downloaded from the GDC legacy database between December 2016 and June 2017

Expert Annotation: Two trained pathologists selected at least three representative tumor regions per slide

Quality Control: 926 slides were removed due to various quality issues (poor staining, low resolution, focus problems, etc.)

Patch Extraction: 10 patches were randomly cropped at 6 different magnification levels from each annotated region

File Structure

Files are organized using the following format:

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[cancer_type]/[resolution]/[TCGA Barcode]/[region]-[number]-[pixel resolution].jpg

Resolution Key

0: 0.5 μm/pixel

1: 0.6 μm/pixel

2: 0.7 μm/pixel

3: 0.8 μm/pixel

4: 0.9 μm/pixel

5: 1.0 μm/pixel

License

Non-Commercial Use: CC-BY-NC-SA 4.0

Commercial Use: Please contact [e-mail] for licensing

Citation

If you use this dataset in your research, please cite:

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Komura, D., et al. (2022). Universal encoding of pan-cancer histology by deep texture representations.
Cell Reports 38, 110424. https://doi.org/10.1016/j.celrep.2022.110424

For Model Benchmarking

If you're interested in using this dataset for benchmarking foundation models or feature extractors, we recommend accessing the dataset through the Hugging Face Hub at dakomura/tcga-ut. The Hugging Face version provides:

Predefined train/validation/test splits (both internal and external facility-based splits)

Ready-to-use benchmarking framework for foundation models

WebDataset format support for efficient data loading

Example implementations for state-of-the-art model evaluation