--- 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//single_1024//.jpeg features/ └── tcga_diagnostic//single_1024// ├── _sd3_vae.npy # (32, 128, 128) float16 (mean+std, 16 ch each) └── _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} } ```