| title | BigCell: Generating Gigapixel Whole-Slide Images |
|---|---|
| authors | Srikar Yellapragada, Alexandros Graikos, Zilinghan Li, Kostas Triaridis, Tarak Nath Nandi, Ji Dong K Bai, Beatrice Knudsen, Tahsin Kurc, Prateek Prasanna, Rajarsi R. Gupta, Ravi K Madduri, Joel Saltz, Dimitris Samaras |
| date | 2026-10-09 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.14261 |
| affiliation | CVLab @ Stony Brook University |
| upvotes | 7 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Whole-slide images (WSIs) are gigapixel-scale digital scans of histopathology tissue and serve as the primary basis for cancer diagnosis. Existing histopathology generative models operate at the patch scale, producing fixed-size tiles of at most 1024times1024 pixels which encompass only a small fraction of the WSI. In contrast, text reports for clinical diagnoses and slide-level diagnostic labels -- the supervision that exists at scale -- operate at the whole-slide level. We present BigCell, the first approach to generate coherent whole-slide histopathology images at native gigapixel resolution. BigCell trains a flow-matching transformer at 2048times2048 pixels on over 120,000 publicly available WSIs spanning multiple organ types. Conditioning on embeddings from pathology foundation models and using an inference-time sliding-window pipeline, BigCell generates coherent images of up to 32,768times32,768 pixels, outperforming prior tile generators on both tile-level and slide-level fidelity metrics. To make gigapixel synthesis practical, we introduce an adaptive scheduling strategy that prioritizes important slide regions, speeding up image generation by over 3\ttimes with minimal quality degradation. Furthermore, we propose the first text-to-image synthesis at 8k resolution by training a separate model that produces dense conditioning grids directly from pathology text reports. We demonstrate that augmenting with synthetic samples improves slide-level few-shot classification performance by up to 18 % over baselines trained on real data. |
cite
@misc{yellapragada2026bigcell,
title={BigCell: Generating Gigapixel Whole-Slide Images},
author={Srikar Yellapragada and Alexandros Graikos and Zilinghan Li and Kostas Triaridis and Tarak Nath Nandi and Ji Dong K Bai and Beatrice Knudsen and Tahsin Kurc and Prateek Prasanna and Rajarsi R. Gupta and Ravi K Madduri and Joel Saltz and Dimitris Samaras},
year={2026},
eprint={2610.14261},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.14261}
}