  Large amounts of digitized histopathological data display a promising future
for developing pathological foundation models via self-supervised learning
methods. Foundation models pretrained with these methods serve as a good basis
for downstream tasks. However, the gap between natural and histopathological
images hinders the direct application of existing methods. In this work, we
present PathoDuet, a series of pretrained models on histopathological images,
and a new self-supervised learning framework in histopathology. The framework
is featured by a newly-introduced pretext token and later task raisers to
explicitly utilize certain relations between images, like multiple
magnifications and multiple stains. Based on this, two pretext tasks,
cross-scale positioning and cross-stain transferring, are designed to pretrain
the model on Hematoxylin and Eosin (H&E) images and transfer the model to
immunohistochemistry (IHC) images, respectively. To validate the efficacy of
our models, we evaluate the performance over a wide variety of downstream
tasks, including patch-level colorectal cancer subtyping and whole slide image
(WSI)-level classification in H&E field, together with expression level
prediction of IHC marker, tumor identification and slide-level qualitative
analysis in IHC field. The experimental results show the superiority of our
models over most tasks and the efficacy of proposed pretext tasks. The codes
and models are available at https://github.com/openmedlab/PathoDuet.
