PixCell: A generative foundation model for digital histopathology images

• Published in: ArXiv.org
• Main Authors: Yellapragada, Srikar, Graikos, Alexandros, Li, Zilinghan, Triaridis, Kostas, Belagali, Varun, Kapse, Saarthak, Nandi, Tarak Nath, Madduri, Ravi K, Prasanna, Prateek, Kurc, Tahsin, Gupta, Rajarsi R, Saltz, Joel, Samaras, Dimitris
• Format: Journal Article
• Language: English
• Published: United States Cornell University 05.06.2025
• ISSN: 2331-8422; 2331-8422

The digitization of histology slides has revolutionized pathology, providing massive datasets for cancer diagnosis and research. Contrastive self-supervised and vision-language models have been shown to effectively mine large pathology datasets to learn discriminative representations. On the other hand, generative models, capable of synthesizing realistic and diverse images, present a compelling solution to address unique problems in pathology that involve synthesizing images; overcoming annotated data scarcity, enabling privacy-preserving data sharing, and performing inherently generative tasks, such as virtual staining. We introduce , the first diffusion-based generative foundation model for histopathology. We train PixCell on PanCan-30M, a vast, diverse dataset derived from 69,184 H&E-stained whole slide images covering various cancer types. We employ a progressive training strategy and a self-supervision-based conditioning that allows us to scale up training without any annotated data. PixCell generates diverse and high-quality images across multiple cancer types, which we find can be used in place of real data to train a self-supervised discriminative model. Synthetic images shared between institutions are subject to fewer regulatory barriers than would be the case with real clinical images. Furthermore, we showcase the ability to precisely control image generation using a small set of annotated images, which can be used for both data augmentation and educational purposes. Testing on a cell segmentation task, a mask-guided PixCell enables targeted data augmentation, improving downstream performance. Finally, we demonstrate PixCell's ability to use H&E structural staining to infer results from molecular marker studies; we use this capability to infer IHC staining from H&E images. Our trained models are publicly released to accelerate research in computational pathology.