A domain-invariant feature learning framework for histopathology images

Kun Fang; Guangtai Ding

doi:10.1117/12.3005087

9 October 2023 A domain-invariant feature learning framework for histopathology images

Kun Fang, Guangtai Ding

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127911M (2023) https://doi.org/10.1117/12.3005087
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

In clinical practice, deep learning models commonly encounter performance degradation in actual applications, mainly due to domain shifts caused by heterogeneity from various sources such as scanners, stains, and medical sites. To address this issue, we examine the single-source domain generalization problem - developing a deep network that can withstand unseen domains, using training data from only one source domain. Our solution involves a novel domain-invariant feature learning model, integrating attention normalization and style enhancement modules at shallow layers of the network while exploring the optimal combination strategy in the convolution block. Furthermore, we use color transformation technology to randomly convert images to produce images with different color distributions to eliminate the effects of stains. The experiments demonstrate that our proposed approach achieves state-of-the-art performance in pathological classification tasks and can be extended to unseen domains.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kun Fang and Guangtai Ding "A domain-invariant feature learning framework for histopathology images", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127911M (9 October 2023); https://doi.org/10.1117/12.3005087

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