  Supervised learning is constrained by the availability of labeled data, which
are especially expensive to acquire in the field of digital pathology. Making
use of open-source data for pre-training or using domain adaptation can be a
way to overcome this issue. However, pre-trained networks often fail to
generalize to new test domains that are not distributed identically due to
tissue stainings, types, and textures variations. Additionally, current domain
adaptation methods mainly rely on fully-labeled source datasets. In this work,
we propose Self-Rule to Multi-Adapt (SRMA), which takes advantage of
self-supervised learning to perform domain adaptation, and removes the
necessity of fully-labeled source datasets. SRMA can effectively transfer the
discriminative knowledge obtained from a few labeled source domain's data to a
new target domain without requiring additional tissue annotations. Our method
harnesses both domains' structures by capturing visual similarity with
intra-domain and cross-domain self-supervision. Moreover, we present a
generalized formulation of our approach that allows the framework to learn from
multiple source domains. We show that our proposed method outperforms baselines
for domain adaptation of colorectal tissue type classification \new{in single
and multi-source settings}, and further validate our approach on an in-house
clinical cohort. The code and trained models are available open-source:
https://github.com/christianabbet/SRA.
