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 .

我们提出了一种名为PatchShuffling和IMPaSh的自助对比学习框架，以训练深度学习模型从而获得一个可以提取抗于领域漂移图像表示的ResNet50编码器。我们的方法在对横跨领域的结肠组织图像进行交叉分类时表现出了优于其他传统组织学领域适应和最先进的自监督学习方法的性能。

IMPaSh：一种新颖的抗域漂移表征方法用于结直肠癌组织分类