  Self-supervised learning (SSL) enables learning useful inductive biases
through utilizing pretext tasks that require no labels. The unlabeled nature of
SSL makes it especially important for whole slide histopathological images
(WSIs), where patch-level human annotation is difficult. Masked Autoencoders
(MAE) is a recent SSL method suitable for digital pathology as it does not
require negative sampling and requires little to no data augmentations.
However, the domain shift between natural images and digital pathology images
requires further research in designing MAE for patch-level WSIs. In this paper,
we investigate several design choices for MAE in histopathology. Furthermore,
we introduce a multi-modal MAE (MMAE) that leverages the specific
compositionality of Hematoxylin & Eosin (H&E) stained WSIs. We performed our
experiments on the public patch-level dataset NCT-CRC-HE-100K. The results show
that the MMAE architecture outperforms supervised baselines and other
state-of-the-art SSL techniques for an eight-class tissue phenotyping task,
utilizing only 100 labeled samples for fine-tuning. Our code is available at
https://github.com/wisdomikezogwo/MMAE_Pathology
