Stain-Invariant Representation for Tissue Classification in Histology Images

• URL: http://arxiv.org/abs/2411.15237v1
• Date: Thu, 21 Nov 2024 23:50:30 GMT
• Title: Stain-Invariant Representation for Tissue Classification in Histology Images
• Authors: Manahil Raza, Saad Bashir, Talha Qaiser, Nasir Rajpoot,
• Abstract summary: We propose a framework that generates stain-augmented versions of the training images using stain perturbation matrix. We evaluate the performance of the proposed model on cross-domain multi-class tissue type classification of colorectal cancer images.
• Score: 1.1624569521079424
• License:
• Abstract: The process of digitising histology slides involves multiple factors that can affect a whole slide image's (WSI) final appearance, including the staining protocol, scanner, and tissue type. This variability constitutes a domain shift and results in significant problems when training and testing deep learning (DL) algorithms in multi-cohort settings. As such, developing robust and generalisable DL models in computational pathology (CPath) remains an open challenge. In this regard, we propose a framework that generates stain-augmented versions of the training images using stain matrix perturbation. Thereafter, we employed a stain regularisation loss to enforce consistency between the feature representations of the source and augmented images. Doing so encourages the model to learn stain-invariant and, consequently, domain-invariant feature representations. We evaluate the performance of the proposed model on cross-domain multi-class tissue type classification of colorectal cancer images and have achieved improved performance compared to other state-of-the-art methods.

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