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 .
@article{arxiv.2208.11052,
title = {IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue Classification},
author = {Trinh Thi Le Vuong and Quoc Dang Vu and Mostafa Jahanifar and Simon Graham and Jin Tae Kwak and Nasir Rajpoot},
journal= {arXiv preprint arXiv:2208.11052},
year = {2022}
}