sofieneb/conch_v15_features

CONCH v1.5 Patch Features for TCGA and CPTAC

Pre-extracted patch-level embeddings from the CONCH v1.5 pathology foundation model for 11,760 whole-slide images (WSIs): 9,838 from TCGA (32 projects) and 1,922 from CPTAC (9 cohorts).

Features were extracted with TRIDENT. They are meant for weakly supervised slide-level tasks, such as multiple-instance learning (MIL) for classification, survival or biomarker prediction, without having to download or process the raw WSIs.

These features were produced for the study From Patches to Patients: A study of the tile-to-slide performance transferability in Digital Pathology. If you use them, please cite it (see Citation).

Extraction settings

Setting	Value
Pipeline	TRIDENT (run_batch_of_slides.py --task all: tissue segmentation, patching, feature extraction)
Patch encoder	conch_v15 (MahmoodLab/conchv1_5)
Embedding dimension	768
Patch size	512 x 512 px
Magnification	20X
Patch overlap	none
Unit	one .h5 file per WSI

Slides that failed during processing were skipped (--skip_errors), so a small number of slides from the original cohorts may be missing.

Repository structure

TCGA/
  TCGA-ACC/
    TCGA-XX-XXXX-01Z-00-DX1.<uuid>.h5
    ...
  TCGA-BLCA/
  ...
CPTAC/
  CPTAC_BRCA/
  ...

Each file is named after the source slide, so TCGA files keep the full slide barcode and UUID.

Cohorts

TCGA (9,838 slides)

Project	Slides	Project	Slides	Project	Slides
TCGA-ACC	227	TCGA-BLCA	457	TCGA-BRCA	1,133
TCGA-CESC	279	TCGA-CHOL	38	TCGA-COAD	458
TCGA-DLBC	44	TCGA-ESCA	158	TCGA-GBM	195
TCGA-HNSC	443	TCGA-KICH	77	TCGA-KIRC	519
TCGA-KIRP	298	TCGA-LGG	491	TCGA-LIHC	359
TCGA-LUAD	541	TCGA-LUSC	512	TCGA-MESO	75
TCGA-OV	83	TCGA-PAAD	176	TCGA-PCPG	176
TCGA-PRAD	401	TCGA-READ	166	TCGA-SARC	253
TCGA-SKCM	432	TCGA-STAD	438	TCGA-TGCT	149
TCGA-THCA	504	TCGA-THYM	117	TCGA-UCEC	502
TCGA-UCS	57	TCGA-UVM	80

CPTAC (1,922 slides)

The CPTAC slides were selected to match those available in the Patho-Bench library, so these features can be used directly with its task splits.

Cohort	Slides	Cohort	Slides	Cohort	Slides
CPTAC_BRCA	112	CPTAC_CCRCC	245	CPTAC_COAD	98
CPTAC_GBM	243	CPTAC_HNSCC	259	CPTAC_LSCC	304
CPTAC_LUAD	326	CPTAC_PDA	242	CPTAC_UCEC	93

File format

Each .h5 file contains:

• features: float array of shape (N, 768), one CONCH v1.5 embedding per tissue patch
• coords: int array of shape (N, 2), the (x, y) top-left coordinate of each patch in level-0 pixel space of the original WSI

Extraction metadata, such as patch size and magnification, is stored as HDF5 attributes.

Download

pip install -U huggingface_hub hf_transfer
export HF_HUB_ENABLE_HF_TRANSFER=1   # optional, faster downloads

You can download a single cohort, several cohorts, a whole source, a single slide, or everything. Pick the folder(s) you need with --include (CLI) or allow_patterns (Python).

Command line

# One cohort (e.g. TCGA-BRCA)
hf download sofieneb/conch_v15_features --repo-type dataset \
  --include "TCGA/TCGA-BRCA/*" --local-dir ./conch_v15_features

# Several cohorts (e.g. NSCLC = LUAD + LUSC)
hf download sofieneb/conch_v15_features --repo-type dataset \
  --include "TCGA/TCGA-LUAD/*" "TCGA/TCGA-LUSC/*" --local-dir ./conch_v15_features

# One CPTAC cohort
hf download sofieneb/conch_v15_features --repo-type dataset \
  --include "CPTAC/CPTAC_GBM/*" --local-dir ./conch_v15_features

# All of CPTAC
hf download sofieneb/conch_v15_features --repo-type dataset \
  --include "CPTAC/*" --local-dir ./conch_v15_features

# Everything
hf download sofieneb/conch_v15_features --repo-type dataset \
  --local-dir ./conch_v15_features

With older versions of huggingface_hub, replace hf download with huggingface-cli download.

Python

from huggingface_hub import snapshot_download, hf_hub_download

REPO = "sofieneb/conch_v15_features"

# One cohort
snapshot_download(REPO, repo_type="dataset",
                  allow_patterns=["TCGA/TCGA-BRCA/*"],
                  local_dir="./conch_v15_features")

# Several cohorts, e.g. RCC subtypes (KIRC + KIRP + KICH)
snapshot_download(REPO, repo_type="dataset",
                  allow_patterns=["TCGA/TCGA-KIRC/*", "TCGA/TCGA-KIRP/*", "TCGA/TCGA-KICH/*"],
                  local_dir="./conch_v15_features")

# A single slide
hf_hub_download(REPO, repo_type="dataset",
                filename="TCGA/TCGA-BRCA/<slide_id>.h5",
                local_dir="./conch_v15_features")

To see which cohorts and files are available before downloading:

from huggingface_hub import HfApi
from collections import Counter

files = HfApi().list_repo_files("sofieneb/conch_v15_features", repo_type="dataset")
print(Counter("/".join(f.split("/")[:2]) for f in files if f.endswith(".h5")))

Downloads resume automatically if interrupted. Just rerun the same command.

Usage

Load a slide's features:

import h5py
import torch

with h5py.File("conch_v15_features/TCGA/TCGA-BRCA/<slide_id>.h5", "r") as f:
    feats = torch.from_numpy(f["features"][:])   # (N, 768)
    coords = f["coords"][:]                      # (N, 2)

Build a slide table for one cohort:

from pathlib import Path
import pandas as pd

root = Path("conch_v15_features/TCGA/TCGA-BRCA")
df = pd.DataFrame({"path": sorted(root.glob("*.h5"))})
df["slide_id"] = df["path"].map(lambda p: p.stem)
df["case_id"] = df["slide_id"].str.slice(0, 12)   # TCGA-XX-XXXX, for patient-level splits

Important notes

• Features are per slide, not per patient. Some patients have several slides. Split train and test sets by patient (the TCGA case ID) to avoid data leakage.
• No labels are included. Clinical, molecular and survival labels must be taken from the GDC (TCGA) and PDC/TCIA (CPTAC), or from Patho-Bench for the CPTAC tasks.
• Features depend on the preprocessing. Tissue segmentation, patch size and magnification all affect the embeddings. Features from a different pipeline or encoder are not interchangeable with these.

Source data and licensing

• TCGA slides: NCI Genomic Data Commons
• CPTAC slides: The Cancer Imaging Archive (TCIA)
• CONCH v1.5 weights are released by the Mahmood Lab under CC BY-NC-ND 4.0, for non-commercial academic research only. These features are derived from that model, and this dataset uses the same license. Use of the features must also follow the TCGA and CPTAC/TCIA data use policies.

Citation

If you use these features, please cite:

@article{boutaj2026patches,
  title={From Patches to Patients: A study of the tile-to-slide performance transferability in Digital Pathology},
  author={Boutaj, Sofi{\`e}ne and Fillioux, Leo and Vakalopoulou, Maria and Christodoulidis, Stergios and Marza, Pierre},
  journal={arXiv preprint arXiv:2606.10778},
  year={2026}
}

Please also cite the encoder, the extraction pipeline and the source datasets:

• CONCH: Lu, M. Y. et al. A visual-language foundation model for computational pathology. Nature Medicine (2024).
• CONCH v1.5 / TITAN: Ding, T. et al. Multimodal Whole Slide Foundation Model for Pathology. arXiv:2411.19666 (2024).
• TRIDENT: Zhang, A. et al. Accelerating Data Processing and Benchmarking of AI Models for Pathology. arXiv:2502.06750 (2025).
• TCGA: The Cancer Genome Atlas Research Network, https://www.cancer.gov/tcga
• CPTAC: National Cancer Institute Clinical Proteomic Tumor Analysis Consortium, https://proteomics.cancer.gov/programs/cptac