  Self-supervised learning (SSL) has emerged as a key technique for training
networks that can generalize well to diverse tasks without task-specific
supervision. This property makes SSL desirable for computational pathology, the
study of digitized images of tissues, as there are many target applications and
often limited labeled training samples. However, SSL algorithms and models have
been primarily developed in the field of natural images and whether their
performance can be improved by adaptation to particular domains remains an open
question. In this work, we present an investigation of modifications to SSL for
pathology data, specifically focusing on the DINOv2 algorithm. We propose
alternative augmentations, regularization functions, and position encodings
motivated by the characteristics of pathology images. We evaluate the impact of
these changes on several benchmarks to demonstrate the value of tailored
approaches.
