  Whole slide imaging is fundamental to biomedical microscopy and computational
pathology. Previously, learning representations for gigapixel-sized whole slide
images (WSIs) has relied on multiple instance learning with weak labels, which
do not annotate the diverse morphologic features and spatial heterogeneity of
WSIs. A high-quality self-supervised learning method for WSIs would provide
transferable visual representations for downstream computational pathology
tasks, without the need for dense annotations. We present Slide Pre-trained
Transformers (SPT) for gigapixel-scale self-supervision of WSIs. Treating WSI
patches as tokens, SPT combines data transformation strategies from language
and vision modeling into a general and unified framework to generate views of
WSIs for self-supervised pretraining. SPT leverages the inherent regional
heterogeneity, histologic feature variability, and information redundancy
within WSIs to learn high-quality whole slide representations. We benchmark SPT
visual representations on five diagnostic tasks across three biomedical
microscopy datasets. SPT significantly outperforms baselines for
histopathologic diagnosis, cancer subtyping, and genetic mutation prediction.
Finally, we demonstrate that SPT consistently improves whole slide
representations when using off-the-shelf, in-domain, and foundational patch
encoders for whole slide multiple instance learning.
