  Computational pathology can lead to saving human lives, but models are
annotation hungry and pathology images are notoriously expensive to annotate.
Self-supervised learning has shown to be an effective method for utilizing
unlabeled data, and its application to pathology could greatly benefit its
downstream tasks. Yet, there are no principled studies that compare SSL methods
and discuss how to adapt them for pathology. To address this need, we execute
the largest-scale study of SSL pre-training on pathology image data, to date.
Our study is conducted using 4 representative SSL methods on diverse downstream
tasks. We establish that large-scale domain-aligned pre-training in pathology
consistently out-performs ImageNet pre-training in standard SSL settings such
as linear and fine-tuning evaluations, as well as in low-label regimes.
Moreover, we propose a set of domain-specific techniques that we experimentally
show leads to a performance boost. Lastly, for the first time, we apply SSL to
the challenging task of nuclei instance segmentation and show large and
consistent performance improvements under diverse settings.
