SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markers

Download Data | Reproducibility | Notebooks | Pre-encoded UNI embeddings | GitHub Repository | Citation

Abstract:

SurGen is a publicly available colorectal cancer dataset comprising 1,020 whole slide images (WSIs) from 843 cases. Each WSI is digitised at 40× magnification (0.1112 µm/pixel) and is accompanied by key genetic marker; KRAS, NRAS, BRAF as well as mismatch repair (MMR) status and five-year survival data (available for 426 cases). This repository provides standard train-validation-test splits and example scripts to facilitate machine learning experiments in computational pathology, biomarker discovery, and prognostic modelling. SurGen aims to advance cancer diagnostics by offering a consistent, high-quality dataset with rich annotations, supporting both targeted research on primary colorectal tumours and broader studies of metastatic sites.

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Overview

SurGen is split into two sub-cohorts:

1. SR386 – Primary colorectal cancer (427 WSIs) with five-year survival data
2. SR1482 – Colorectal cancer cases (593 WSIs) including metastatic lesions (liver, lung, peritoneum), with full biomarker data

Each WSI is stored in .CZI format. For convenience, precomputed patch embeddings (extracted using the UNI foundation model) are also available. Refer to the GitHub Repository for usage examples and data-processing scripts.

Reproducibility

The reproducibility directory contains step-by-step instructions to replicate the results shown in the associated paper. These include:

• Details on environment setup and required dependencies.
• Scripts for processing the WSIs and generating patch-level features.
• Guidelines for reproducing survival analysis and biomarker prediction results.

This ensures that all experiments can be reliably reproduced by other researchers using the provided dataset and embeddings.

Notebooks

The notebooks directory provides interactive examples for exploring the SurGen dataset and pre-extracted features:

1. simple_load_wsi_tile.ipynb – Demonstrates how to interact with .CZI files in Python, including reading and viewing from WSIs.
2. patch_feature_extraction.ipynb – Shows how to extract patch-level features using Hugging Face models, leveraging the UNI foundation model.
3. zarr_examined.ipynb – Explains the layout and usage of pre-extracted SurGen features stored in Zarr format, making it easier to integrate with downstream analysis pipelines.

These notebooks provide a practical starting point for using the dataset and applying it to various computational pathology tasks.

Reference

When referencing this work, please use the following:

@article{myles2025surgen,
  author = {Craig Myles and In Hwa Um and Craig Marshall and David Harris-Birtill and David J Harrison},
  title = {SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markers},
  year = {2025},
  note = {Manuscript under review.}
}