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hescape-pyarrow

Tasks:
Image Classification
Feature Extraction
Image Segmentation
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Size:
100K - 1M
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Tags:
spatial-transcriptomics
histology
pathology
benchmark
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 ---
viewer: true
license: cc-by-nc-sa-4.0
language:
- en
tags:
- spatial-transcriptomics
- histology
- pathology
- benchmark
task_categories:
- image-classification
- feature-extraction
- image-segmentation
size_categories:
- 100B<n<1T
extra_gated_prompt: >-
- This dataset and associated code are released under the [CC-BY-NC-ND 4.0 license](https://creativecommons.org/licenses/by-nc-nd/4.0/) and may only be used for non-commercial, academic research purposes with proper attribution.
- Any commercial use, sale, or other monetization of the htan-wustl dataset and its derivatives, which include models trained on outputs from the htan-wustl datasets, is prohibited and requires prior approval.
- By downloading the dataset, you attest that all information (affiliation, research use) is correct and up-to-date. Downloading the dataset requires prior registration on Hugging Face and agreeing to the terms of use. By downloading this dataset, you agree not to distribute, publish or reproduce a copy of the dataset. If another user within your organization wishes to use the htan-wustl dataset, they must register as an individual user and agree to comply with the terms of use. Users may not attempt to re-identify the deidentified data used to develop the underlying dataset.
- This dataset is provided “as-is” without warranties of any kind, express or implied. This dataset has not been reviewed, certified, or approved by any regulatory body, including but not limited to the FDA (U.S.), EMA (Europe), MHRA (UK), or other medical device authorities. Any application of this dataset in healthcare or biomedical settings must comply with relevant regulatory requirements and undergo independent validation. Users assume full responsibility for how they use this dataset and any resulting consequences. The authors, contributors, and distributors disclaim any liability for damages, direct or indirect, resulting from dataset use. Users are responsible for ensuring compliance with data protection regulations (e.g., GDPR, HIPAA) when using it in research that involves patient data.
extra_gated_fields:
Full Name (first and last): text
Type of Affiliation:
type: select
options:
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- Other
Current Affiliation (no abbreviations): text
Current and Official Institutional Email: text
Main use-case:
type: select
options:
- Models Benchmarking
- Biomarker Discovery
- Diagnostics
- Pathology Workflows Acceleration
- Other
Please add information on your intended research use: text
I agree to use this dataset for non-commercial, academic purposes only: checkbox
I agree not to distribute the dataset: checkbox
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---
# HESCAPE • PyArrow Format
HESCAPE (**H&E + Spatial Contrastive Pretraining Benchmark**) is a large-scale benchmark for multimodal learning in spatial transcriptomics.
This repository hosts the **PyArrow-formatted Hugging Face datasets** for HESCAPE, organized by panel as dataset **configs**.
---
## Available Configs (Panels)
This dataset repo exposes the following configs:
- `human-5k-panel`
- `human-breast-panel`
- `human-colon-panel`
- `human-immuno-oncology-panel`
- `human-lung-healthy-panel`
- `human-multi-tissue-panel`
Each config corresponds to an independent HESCAPE dataset panel.
---
## Schema
Each dataset entry contains the following columns:
| Column | Type | Description |
|---------------|-----------|-------------|
| `name` | class_label| Unique identifier for the sample |
| `image` | image | Image patch |
| `gexp` | array | Transcriptomic expression based on gene panel|
| `cell_coords` | array | Coords of the image-gexp pair in tissue |
| `source` | string | Source of data |
| `atlas` | string | Label for atlas |
| `age` | string | Age |
| `diagnosis` | string | Diagnosis : "Cancer" or "None" |
| `cancer` | bool | Whether cancer or not |
| `oncotree_code` | string | Oncotree code |
| `tissue ` | class_label | Tissue label |
| `tumor_grade` | string | Grade of tumor |
| `gender` | string | Gender |
| `race` | string | Race |
| `treatment_type` | string | Treatement type |
| `therapeutic_agents` | string | Therapeutic agent |
| `tumor_tissue_type` | string | Tumor tissue type |
| `assay` | string | Assay used |
| `preservation_method` | string | Preservation method used |
| `stain` | string | Stain of histology |
| `spaceranger` | string | Spaceranger version |
| `species` | string | Species |
| `cytassist` | string | Boolean |
---
## Usage
Load a specific panel (config):
```python
from datasets import load_dataset
# Example: load the human breast panel
ds = load_dataset(
"Peng-AI/hescape-pyarrow",
name="human-breast-panel",
split="train",
streaming=True
)
print(ds)
```
## List all configs
```python
from datasets import get_dataset_config_names
get_dataset_config_names("Peng-AI/hescape-pyarrow")
```
### How to cite:
```
@misc{gindra2025largescalebenchmarkcrossmodallearning,
title={A Large-Scale Benchmark of Cross-Modal Learning for Histology and Gene Expression in Spatial Transcriptomics},
author={Rushin H. Gindra and Giovanni Palla and Mathias Nguyen and Sophia J. Wagner and Manuel Tran and Fabian J Theis and Dieter Saur and Lorin Crawford and Tingying Peng},
year={2025},
eprint={2508.01490},
archivePrefix={arXiv},
primaryClass={q-bio.GN},
url={https://arxiv.org/abs/2508.01490},
}
```
### Contact:
- <b>Rushin Gindra</b> Helmholtz Munich, Munich (`rushin.gindra@helmholtz-munich.de`)
-
<i>The dataset is distributed under the Attribution-NonCommercial-ShareAlike 4.0 International license (CC BY-NC-SA 4.0 Deed)</i>