TC-SSA WSI Feature Bags

This public repository contains pre-extracted whole-slide image patch features used by TC-SSA and SSAMIL experiments. Binary feature bags are organized by feature source and encoder. The Dataset Viewer intentionally displays the file index in metadata/file_index.csv; it does not attempt to interpret the HDF5 or PyTorch feature files as tabular datasets.

Inventory

CONCHv1.5 features

Dataset	Files	Magnification / patch size	Format	Feature dim
BRACS	547	20x / 256 px	HDF5	768
CAMELYON17	1,000	40x / 256 px	HDF5	768
TCGA-BRCA	1,129	20x / 256 px	HDF5	768
TCGA-LUAD	540	20x / 256 px	HDF5	768

Each HDF5 file contains a features dataset with shape (num_patches, 768). Some files also contain patch coordinates.

CPathPatchFeature TCGA-BRCA subset

Encoder / artifact	Files	Format	Feature dim
CHIEF / CTransPath	1,133	PyTorch tensor	768
GigaPath	1,133	PyTorch tensor	1,536
ResNet-50	1,133	PyTorch tensor	1,024
UNI v1	1,133	PyTorch tensor	1,024
Patch coordinates	1,133	HDF5	n/a

The four encoder directories have identical slide filename sets. The 1,125 slides used by the formal TCGA-BRCA experiment splits were validated for full coverage and expected feature dimensions. Missing files from the upstream BRCA subset were locally re-extracted and merged with the released collection.

Layout

features/
  conch_v15/
    BRACS/*.h5
    CAMELYON17/*.h5
    TCGA-BRCA/*.h5
    TCGA-LUAD/*.h5
  cpath_patch_feature/
    brca/
      chief/pt_files/*.pt
      gigap/pt_files/*.pt
      r50/pt_files/*.pt
      uni/pt_files/*.pt
      patches/*.h5
metadata/
  file_index.csv

Dataset Viewer and access

The Viewer exposes a searchable index rather than loading hundreds of gigabytes of heterogeneous binary tensors:

from datasets import load_dataset

index = load_dataset("OzzyChen97/TC-SSA", "file-index", split="train")
print(index[0])

Download selected files with hf download, then load HDF5 features with h5py.File(...) or PyTorch tensors with torch.load(..., map_location="cpu").

Provenance and references

The CPath encoder features and patch coordinates are derived from Dearcat/CPathPatchFeature, released with the work Revisiting End-to-End Learning with Slide-level Supervision in Computational Pathology. Please cite that repository and its associated publications when using the CPath subset.

The CONCH feature bags use CONCH, a pathology vision-language foundation model. The CPath collection uses UNI, CHIEF, GigaPath, and ResNet-50.

Original WSI datasets and access pages:

• BRACS dataset and paper
• CAMELYON17 challenge data
• TCGA-BRCA at the NCI Genomic Data Commons
• TCGA-LUAD at the NCI Genomic Data Commons

The upstream CPathPatchFeature repository additionally contains derived features for TCGA-BLCA, TCGA-NSCLC, CPTAC-NSCLC, CAMELYON, and PANDA. Those additional cohorts are referenced upstream and are not duplicated in this TC-SSA repository.

Citation

@article{lu2024conch,
  title   = {A visual-language foundation model for computational pathology},
  author  = {Lu, Ming Y. and others},
  journal = {Nature Medicine},
  volume  = {30},
  pages   = {863--874},
  year    = {2024},
  doi     = {10.1038/s41591-024-02856-4}
}

@misc{tang2025revisitingendtoend,
  title         = {Revisiting End-to-End Learning with Slide-level Supervision in Computational Pathology},
  author        = {Tang, Wenhao and Qin, Rong and Fang, Heng and Zhou, Fengtao and Chen, Hao and Li, Xiang and Cheng, Ming-Ming},
  year          = {2025},
  eprint        = {2506.02408},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV}
}

Please also cite the original WSI dataset and encoder papers relevant to the files used in your study.