README.md

---
license: cc-by-4.0
task_categories:
  - unconditional-image-generation
tags:
  - histopathology
  - pathology
  - digital-pathology
  - pixcell
  - diffusion
pretty_name: PixCell Sample Data
---

# PixCell — Sample Data

A small bundle of **pre-extracted features** for quickly testing [PixCell](https://github.com/cvlab-stonybrook/PixCell) sampling without preparing the full dataset.

It contains **32 patches** (1024×1024) from 4 TCGA diagnostic whole-slide images across 4 cancer subtypes (BRCA, LUAD, COAD, PRAD), with the images, SD-3.5 VAE latents, and UNI2-h embeddings already extracted.

## Layout

```
patches/
├── metadata/patch_names_all.hdf5          # index; key: tcga_diagnostic_1024
└── tcga_diagnostic/<subtype>/single_1024/<WSI>/<r_c>.jpeg
features/
└── tcga_diagnostic/<subtype>/single_1024/<WSI>/
    ├── <r_c>_sd3_vae.npy                   # (32, 128, 128) float16  (mean+std, 16 ch each)
    └── <r_c>_uni.npy                       # (16, 1536) float16      (4x4 UNI token grid)
```

## Usage

Download the dataset and point `data["root"]` in your PixCell inference `config.py`
(`configs/pan_cancer/pixcell_256_inference.py` / `pixcell_1024_inference.py`) at the
extracted folder:

```python
from huggingface_hub import snapshot_download
root = snapshot_download("StonyBrook-CVLab/PixCell-sample-data", repo_type="dataset")
```

Then run `tools/sample_256.py` / `tools/sample_1024.py` as described in the
[PixCell README](https://github.com/cvlab-stonybrook/PixCell).

## Source & license

Images are derived from [TCGA](https://portal.gdc.cancer.gov/) diagnostic slides,
which are publicly available. Released under CC-BY-4.0.

## Citation

```bibtex
@article{yellapragada2025pixcell,
  title={PixCell: A generative foundation model for digital histopathology images},
  author={Yellapragada, Srikar and Graikos, Alexandros and Li, Zilinghan and Triaridis, Kostas and Belagali, Varun and Kapse, Saarthak and Nandi, Tarak Nath and Madduri, Ravi K and Prasanna, Prateek and Kurc, Tahsin and others},
  journal={arXiv preprint arXiv:2506.05127},
  year={2025}
}
```