  Foundation models are rapidly being developed for computational pathology
applications. However, it remains an open question which factors are most
important for downstream performance with data scale and diversity, model size,
and training algorithm all playing a role. In this work, we present the result
of scaling both data and model size, surpassing previous studies in both
dimensions, and introduce two new models: Virchow 2, a 632M parameter vision
transformer, and Virchow 2G, a 1.85B parameter vision transformer, each trained
with 3.1M histopathology whole slide images. To support this scale, we propose
domain-inspired adaptations to the DINOv2 training algorithm, which is quickly
becoming the default method in self-supervised learning for computational
pathology. We achieve state of the art performance on twelve tile-level tasks,
as compared to the top performing competing models. Our results suggest that
data diversity and domain-specific training can outperform models that only
scale in the number of parameters, but, on average, performance benefits from
domain-tailoring, data scale, and model scale.
