Hugging Face's logo

Datasets:
shannonhill
/
HyperMIL-data

Modalities:
Tabular
Text
Formats:
json
Languages:
English
Size:
< 1K
Tags:
histopathology
computational-pathology
survival-analysis
multiple-instance-learning
hypergraph-learning
graph-learning
Libraries:
Datasets
pandas
Polars
Dataset card Data Studio Files
xet
 Community
1
HyperMIL-data / README.md
shannonhill's picture
shannonhill
Link current single-model checkpoint release
1983326 verified
preview code
|
Raw History Blame Contribute Delete
6.37 kB
metadata 
language:
  - en
tags:
  - histopathology
  - computational-pathology
  - survival-analysis
  - multiple-instance-learning
  - hypergraph-learning
  - graph-learning
  - tcga
pretty_name: >-
  HyperMIL: Whole-Slide Histopathology Features and Graph Representations for
  Survival Analysis

HyperMIL: Whole-Slide Histopathology Features and Graph Representations for Survival Analysis

GitHub: code and evaluation · Pretrained models · Feature extraction and graph construction

Overview

This dataset provides pre-extracted patch features, aligned patch coordinates, and precomputed graph and group representations for 1,344 diagnostic whole-slide images (WSIs) from four TCGA cancer cohorts. It supports survival prediction with multiple-instance learning, graph neural networks, and hypergraph neural networks.

The data accompany the HyperMIL code repository. The supplied feature and graph files can be loaded directly by its training and evaluation scripts, without rerunning preprocessing.

Cohorts

TCGA cohort Cancer type Patients WSIs Data size (GB)
STAD Stomach adenocarcinoma 387 413 35.05
THCA Thyroid carcinoma 505 518 58.40
CHOL Cholangiocarcinoma 39 39 6.43
LIHC Liver hepatocellular carcinoma 360 374 42.99
Total 1,291 1,344 142.87

Sizes use decimal GB. The release contains 12,096 NumPy files. Overall-survival times, event indicators, patient-level five-fold splits, slide mappings, and fixed patch-selection indices are supplied in the companion GitHub repository under splits/ and manifests/. All slides belonging to a patient remain in the same fold.

Files and representations

Directory Contents Files Size (GB) Used by
data/WSI_data/TCGA-<COHORT>/patch_ft/ Frozen ResNet-34 patch features: one float32 .npy array of shape [N, 512] per WSI 1,344 137.014 Shared feature inputs for all methods
data/WSI_data/TCGA-<COHORT>/patch_coor/ Patch coordinates: one .npy array of shape [N, 2] per WSI, aligned row-for-row with its feature array 1,344 0.535 Spatial graph construction and spatial methods
data/hypergraphs/ Sparse structural (MGAS) and semantic (PASG) incidence matrices: two .npz files per WSI 2,688 0.874 HyperMIL and its hypergraph ablations
data/patchgcn_graphs/ PatchGCN graph connectivity in .npz format 1,344 0.894 PatchGCN
data/KNN/ Spatial and feature-space nearest-neighbor graph connectivity in .npz format 2,688 3.491 DualGCN-MIL
data/kmeans_groups/ Spatial and semantic K-means patch-group inputs in .npz format 2,688 0.058 Spatial/Semantic-KMeans-GroupPool

N is the number of retained patches in a WSI and varies between slides. Features are stored once and shared across methods; graph files refer to that same patch ordering. PatchGCN graph files store connectivity without duplicating the feature arrays.

Both MGAS and PASG graph files are provided for all four cohorts.

Directory layout

The following example uses STAD; the same layout applies to THCA, CHOL, and LIHC.

data/
  WSI_data/
    TCGA-STAD/
      patch_ft/<slide_id>_fts.npy
      patch_coor/<slide_id>_coors.npy
  hypergraphs/
    STAD_MGAS_V2/<slide_id>.npz
    STAD_PASG_V2_FINAL/<slide_id>.npz
  patchgcn_graphs/
    TCGA-STAD/<slide_id>.npz
  KNN/
    TCGA-STAD/
      spatial/<slide_id>_knn_spatial_k8.npz
      feature/<slide_id>_knn_feature_k6.npz
  kmeans_groups/
    Spatial-KMeans-GroupPool/cache/STAD/<slide_id>.npz
    Semantic-KMeans-GroupPool/cache/STAD/<slide_id>.npz
  • data_manifest.csv lists every data file's relative path, representation category, byte size, and SHA256.
  • dataset_info.json records the cohort list, total size, file count, and identities of the data manifest and patient splits.
  • release.json identifies the complete, verified data revision used by the companion downloader.

The directory names are retained for compatibility with the released loaders. For graph-array schemas and preprocessing parameters, see the construction documentation and model interfaces.

Download and evaluate

Run the supplied downloader from the code repository so files are installed directly into the expected relative paths:

git clone https://github.com/shannonchen37/HyperMIL.git
cd HyperMIL
python -m pip install -r requirements.txt

# Download the prepared data and pretrained models for STAD.
python scripts/download_data.py --cohort STAD
python scripts/download_models.py --cohort STAD

# Evaluate HyperMIL across all five patient folds.
python scripts/evaluate.py --config configs/hypermil.yaml --cohort STAD

Use STAD, THCA, CHOL, or LIHC to select a cohort. Omit --cohort from both download commands to obtain all four cohorts. Downloads resume when the same command is rerun. The supplied layout requires no manual path edits or Hugging Face login.

Methods are selected through YAML configurations within the same training and evaluation framework. Keep feature ordering, graph inputs, patient splits, and method configurations together when using the released checkpoints. See the GitHub README for installation and evaluation details.

Source and accompanying resources

The underlying histopathology slides originate from TCGA through the NCI Genomic Data Commons. This repository distributes derived feature and graph representations; it does not include raw WSI images. The companion GitHub repository provides preprocessing code, patient splits, method configurations, training and evaluation code, and links to pretrained models.