ogutsevda/graph-tcga-brca
Graph-TCGA-BRCA: A Cell-Graph Dataset for Breast Cancer from TCGA-BRCA Graph-TCGA-BRCA is a graph-level classification dataset derived from the TCGA-BRCA histopathology dataset. Each 224x224 patch image is converted into a cell-graph where nodes represent detected cell nuclei and edges encode spatial proximity, enabling graph-based learning for fine-grained breast lesion subtyping across 2 clinically relevant classes. Note that node features describe cell morphology… See the full description on the dataset page: https://huggingface.co/datasets/ogutsevda/graph-tcga-brca.
Graph-TCGA-BRCA: A Cell-Graph Dataset for Breast Cancer from TCGA-BRCA
<p align="center"> <img src="preprocessing.png" width="600"/> </p>
Graph-TCGA-BRCA is a graph-level classification dataset derived from the TCGA-BRCA histopathology dataset. Each 224x224 patch image is converted into a cell-graph where nodes represent detected cell nuclei and edges encode spatial proximity, enabling graph-based learning for fine-grained breast lesion subtyping across 2 clinically relevant classes. Note that node features describe cell morphology, texture, and color intensity whereas edge features are Euclidean distance in micrometers.
This dataset is part of the paper GrapHist: Graph Self-Supervised Learning for Histopathology.
⚠️ Edge Weight Note: While the architecture in GrapHist supports both positive and negative edge weights, by default edge features represent Euclidean distances—meaning farther nodes have larger, positive values. This can be counterintuitive for many graph neural network models. We recommend experimenting with edge weights, such as using their inverse (e.g.,1/distance) or negative distance (e.g.,-distance), to better capture proximity and benefit learning.
Dataset Summary
Classes
Data Structure
graph-tcga-brca/
├── README.md
├── metadata.csv # graph_path, sample_id, wsi_x, wsi_y, label, split
├── normalization.json # normalizer values for node and edge features computed from train patches
├── preprocessing.png
└── data/
├── graph-data-000000.tar
├── graph-data-000001.tar
└── ... Each .tar file contains ~1 GB of .pt files. Please extract them into the data folder to use our code. These .pt files are PyTorch Geometric Data objects with the following attributes:
Quick Start
import torch
from torch_geometric.data import Data
# Load a single graph
graph = torch.load("data/TCGA-3C-AALI-01Z-00-DX1.F6E9A5DF-D8FB-45CF-B4BD-C6B76294C291_x55517_y57392.pt", weights_only=False)
print(graph)
# Data(x=[26, 96], edge_index=[2, 67], edge_attr=[67, 1], sample_id='TCGA-3C-AALI-01Z-00-DX1.F6E9A5DF-D8FB-45CF-B4BD-C6B76294C291', label='Infiltrating duct carcinoma, NOS', wsi_x='55517', wsi_y='57392')
print(f"Nodes: {graph.x.shape[0]}, Edges: {graph.edge_index.shape[1]}")
# Nodes: 26, Edges: 67Citation
If you use this dataset, please cite both our work, and the original TCGA-BRCA dataset:
GrapHist (this dataset):
@misc{ogut2026graphist,
title={GrapHist: Graph Self-Supervised Learning for Histopathology},
author={Sevda Öğüt and Cédric Vincent-Cuaz and Natalia Dubljevic and Carlos Hurtado and Vaishnavi Subramanian and Pascal Frossard and Dorina Thanou},
year={2026},
eprint={2603.00143},
url={https://arxiv.org/abs/2603.00143},
}TCGA-BRCA (source images):
@article{weinstein2013cancer,
title={The cancer genome atlas pan-cancer analysis project},
author={Weinstein, John N and Collisson, Eric A and Mills, Gordon B and Shaw, Kenna R and Ozenberger, Brad A and Ellrott, Kyle and Shmulevich, Ilya and Sander, Chris and Stuart, Joshua M},
journal={Nature Genetics},
volume={45},
number={10},
pages={1113--1120},
year={2013},
publisher={Nature Publishing Group}
}License
This dataset is released under the CC BY-NC-SA 4.0 license.