| Autor: |
Raza, Manahil, Bashir, Saad, Qaiser, Talha, Rajpoot, Nasir |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
In 27th Conference on Medical Image Understanding and Analysis 2023 (p. 242) |
| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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