A regularization term for slide correlation reduction in whole slide image analysis with deep learning

Zhang, H, Meng, Y ORCID: 0000-0001-7344-2174, Qian, X, Yang, X, Coupland, SE ORCID: 0000-0002-1464-2069 and Zheng, Y ORCID: 0000-0002-7873-0922
(2021) A regularization term for slide correlation reduction in whole slide image analysis with deep learning. In: Medical Imaging with Deep Learning (MIDL) 2021, 2021-7-7 - 2021-7-9, Lübeck, Germany.

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To develop deep learning-based models for automatic analysis of histopathology whole slide images (WSIs), the atomic entities to be directly processed are often the smaller patches cropped from WSIs as it is not always possible to feed a whole WSI to a model given its enormous size. However, a trained model tends to relate the slide-specific characteristics to diagnosis results because a large number of patches cropped from the same WSI will share common slide features and thus have strong correlations between them, resulting in deteriorated generalization capability of the trained model. Current approaches to alleviate this issue include data pre-processing (stain normalization or color augmentation) and adversarial learning, both of which introduce extra complications in computations. Alternatively, we propose to reduce the impact of this issue by introducing a new regularization term to the standard loss function to reduce the correlation of the patches from the same WSI. It is intuitive and easy-to-implement and introduces comparably smaller computation overhead compared to existing approaches. Experimental results prove that the proposed regularization term is able to enhance the generalization capability of learning models and consequently to achieve better performance. The code is available in: https://github.com/hrzhang1123/SlideCorrelationReduction.

Item Type: Conference or Workshop Item (Unspecified)
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User: Symplectic Admin
Date Deposited: 11 May 2021 08:55
Last Modified: 06 Jul 2023 11:13
Open Access URL: https://openreview.net/pdf?id=2vCFIoWDS6E
URI: https://livrepository.liverpool.ac.uk/id/eprint/3121746