IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue Classification

• URL: http://arxiv.org/abs/2208.11052v1
• Date: Tue, 23 Aug 2022 15:59:08 GMT
• Title: IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue Classification
• Authors: Trinh Thi Le Vuong, Quoc Dang Vu, Mostafa Jahanifar, Simon Graham, Jin Tae Kwak, Nasir Rajpoot
• Abstract summary: We propose a new augmentation called PatchShuffling and a novel self-supervised contrastive learning framework named IMPaSh for pre-training deep learning models. We show that the proposed method outperforms other traditional histology domain-adaptation and state-of-the-art self-supervised learning methods.
• Score: 5.017246733091823
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 .

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