PixCell: A generative foundation model for digital histopathology images
Journal:
arXiv
Published Date:
Jun 5, 2025
Abstract
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 PixCell, 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.
