  The appearance of histopathology images depends on tissue type, staining and
digitization procedure. These vary from source to source and are the potential
causes for domain-shift problems. Owing to this problem, despite the great
success of deep learning models in computational pathology, a model trained on
a specific domain may still perform sub-optimally when we apply them to another
domain. To overcome this, we propose a new augmentation called PatchShuffling
and a novel self-supervised contrastive learning framework named IMPaSh for
pre-training deep learning models. Using these, we obtained a ResNet50 encoder
that can extract image representation resistant to domain-shift. We compared
our derived representation against those acquired based on other
domain-generalization techniques by using them for the cross-domain
classification of colorectal tissue images. We show that the proposed method
outperforms other traditional histology domain-adaptation and state-of-the-art
self-supervised learning methods. Code is available at:
https://github.com/trinhvg/IMPash .
